{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\zechmeej\Dropbox\lice\InformationAquisition\JEPS Submission Jan. 5 2016\conditional accept\replication files\TADA log file for JEPS.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}18 Dec 2017, 12:10:42

{com}. do "C:\Users\zechmeej\AppData\Local\Temp\STD02000000.tmp"
{txt}
{com}. ***REPLICATION CODE FILE FOR MEROLLA&ZECHMEISTER JEPS 2017/18***
. *Last version date: 12/18/17
. *Created using Stata v14.1
. *Contact info: liz.zechmeister@vanderbilt.edu
. 
. *to run: first open the data file for replication: use "TADA data for JEPS.dta"
. *note: the TADA JEPS dataset has not been modified, except to drop variables not used in the below analyses
. 
. *The below code replicates analyses in Merolla & Zechmeister JEPS Information Acquisition article
. 
. set more off
{txt}
{com}. 
. ****************************************
. /*
> INVENTORY OF VARIABLES IN TADA JEPS DATASET:
> 1. meta data (timing variable): time
> 2. demographics:  country gender ocup edu age
> 3. experiment treatment variables: treatment gt tt tt_rbi tt_rdc dom_tt crime econ control
> 4. emotions measures: emo1a emo1b emo1c emo1d emo1e emo1f emo1g emo1h emo1i emo1j emo2 
> 5. information acquisition measures: mcn1 mcn2 mct1a mct2a mct1b mct2b mct1c mct2c mcdt1 mcdt2 mce1 mce2 mcc1 mcc2 back_mcn1 
> 6. information acquisition measures - measuring whether they returned to article: back_mcn2 back_mct1a back_mct2a back_mct1b back_mct2b back_mct1c back_mct2c back_mce1 back_mce2 back_mcdt1 back_mcdt2 
> */
. *****************************************
. 
. ****************************************
. *********PRE-PROCESS THE DATA***********
. ****************************************
. 
. ***create dummy for those who took under 5 minutes or over 1 hour 
. recode time 1=1 13=1 *=0, gen(time_drop)
{txt}(8074 differences between time and time_drop)

{com}. 
. *drop those 341 individuals who score "1" on the time_drop dummy
. drop if time_drop==1
{txt}(341 observations deleted)

{com}. 
. *create an online dummy for those studies for which mode=online
. gen online=0
{txt}
{com}. recode online 0=1 if country==3
{txt}(online: 943 changes made)

{com}. recode online 0=1 if country==5
{txt}(online: 1151 changes made)

{com}. recode online 0=1 if country==7
{txt}(online: 951 changes made)

{com}. recode online 0=1 if country==8
{txt}(online: 992 changes made)

{com}. recode online 0=1 if country==9
{txt}(online: 1145 changes made)

{com}. 
. *recode gender variable to create a dummy where 1=female
. recode gender 1=1 2=0, gen(female)
{txt}(3739 differences between gender and female)

{com}. 
. *recode occupation variable to a dummy where 1=working full-time
. recode ocup 1=1 2/999=0, gen(workingFT)
{txt}(4182 differences between ocup and workingFT)

{com}. 
. *drop missing/dk/nr on education variable
. recode edu 888=. 988=. 999=.
{txt}(edu: 5 changes made)

{com}. 
. *drop missing/dk/nr on emotion variables
. gen afraid = emo1a
{txt}(9 missing values generated)

{com}. gen anxious = emo1b
{txt}(12 missing values generated)

{com}. gen worried = emo1c
{txt}(12 missing values generated)

{com}. gen enthusiastic = emo1d
{txt}(9 missing values generated)

{com}. gen hopeful = emo1e
{txt}(9 missing values generated)

{com}. gen proud = emo1f
{txt}(8 missing values generated)

{com}. gen hatred = emo1g
{txt}(10 missing values generated)

{com}. gen contempt = emo1h
{txt}(10 missing values generated)

{com}. gen bitterness = emo1i
{txt}(9 missing values generated)

{com}. gen resentful = emo1j
{txt}(9 missing values generated)

{com}. 
. recode afraid 9/999=.
{txt}(afraid: 40 changes made)

{com}. recode anxious 9/999=.
{txt}(anxious: 47 changes made)

{com}. recode worried 9/999=.
{txt}(worried: 32 changes made)

{com}. recode enthusiastic 9/999=.
{txt}(enthusiastic: 49 changes made)

{com}. recode hopeful 9/999=.
{txt}(hopeful: 57 changes made)

{com}. recode proud 9/999=.
{txt}(proud: 59 changes made)

{com}. recode hatred 9/999=.
{txt}(hatred: 53 changes made)

{com}. recode contempt 9/999=.
{txt}(contempt: 60 changes made)

{com}. recode bitterness 9/999=.
{txt}(bitterness: 47 changes made)

{com}. recode resentful 9/999=.
{txt}(resentful: 54 changes made)

{com}. 
. 
. *******************************************
. *********GENERATE DATA FOR TABLE 1*********
. *******************************************
. 
. tab country treatment

              {txt}{c |}                                        Treatment
      country {c |}   Control  Good Time  Terror (n  Terror (w  Economic   Crime Thr  Domestic   Terror (w {c |}     Total
{hline 14}{c +}{hline 88}{c +}{hline 10}
      Albania {c |}{res}       123        121        123        116        117          0          0          0 {txt}{c |}{res}       600 
{txt}      Ecuador {c |}{res}       100        101        101         97          0        101        100          0 {txt}{c |}{res}       600 
{txt}       France {c |}{res}       193        183        184        195        188          0          0          0 {txt}{c |}{res}       943 
{txt}         Peru {c |}{res}       151        153        150        151          0          0        155          0 {txt}{c |}{res}       760 
{txt}        Spain {c |}{res}       202        194        193        181        188          0        192          0 {txt}{c |}{res}     1,150 
{txt}  Turkey FtoF {c |}{res}       126        117        124        125          0          0        112          0 {txt}{c |}{res}       604 
{txt}Turkey online {c |}{res}       192        185        203        191        176          0          0          0 {txt}{c |}{res}       947 
{txt}           UK {c |}{res}       196        190        200        200        204          0          0          0 {txt}{c |}{res}       990 
{txt}           US {c |}{res}       194        186        201        177        196          0          0        191 {txt}{c |}{res}     1,145 
{txt}{hline 14}{c +}{hline 88}{c +}{hline 10}
        Total {c |}{res}     1,477      1,430      1,479      1,433      1,069        101        559        191 {txt}{c |}{res}     7,739 

{txt}
{com}. 
. /*  ******CODING INFORMATION******
> 
> Coding information 1 - raw number codes for treatment variable: 
> 1=Control 2=Good Times 3=Int'l Terror Threat (TT) 4=Int'l TT w/ reminder 5=Economic Threat 6=Crime Threat 7=Domestic TT 8=Terror w/ alt reminder - US Only
> 
> Conding information 2 - raw number codes for country variable:
> 1=Albania 2=Ecuador 3=France 4=Peru 5=Spain 6=Turkey face-to-face 7=Turkey online 8=UK 9=US
> 
> Note: Per footnote 3, conditions 1 (control) and 8 (US alternate reminder condition) not included in table
> */
. 
. ******************************************
. ****GENERATE DATA FOR APPENDIX TABLE 1****
. ******************************************
. 
. sort country
{txt}
{com}. by country: sum age female edu workingFT

{txt}{hline}
-> country = Albania

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        600    39.20333    13.98456         18         82
{txt}{space 6}female {c |}{res}        600         .62    .4857914          0          1
{txt}{space 9}edu {c |}{res}        599    12.12521    3.182206          1         20
{txt}{space 3}workingFT {c |}{res}        597    .4371859    .4964547          0          1

{txt}{hline}
-> country = Ecuador

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        600    38.38333    13.81175         18         81
{txt}{space 6}female {c |}{res}        600    .5083333    .5003477          0          1
{txt}{space 9}edu {c |}{res}        600    11.14333    3.955865          0         18
{txt}{space 3}workingFT {c |}{res}        600          .5    .5004172          0          1

{txt}{hline}
-> country = France

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        943    43.80806    14.41839         18         87
{txt}{space 6}female {c |}{res}        942     .537155    .4988825          0          1
{txt}{space 9}edu {c |}{res}        942    15.55202    4.079048          0         38
{txt}{space 3}workingFT {c |}{res}        943    .4644751     .499001          0          1

{txt}{hline}
-> country = Peru

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        760    39.56447    16.06315         18         86
{txt}{space 6}female {c |}{res}        760    .5026316    .5003223          0          1
{txt}{space 9}edu {c |}{res}        758     11.6438     3.60387          0         17
{txt}{space 3}workingFT {c |}{res}        760    .3657895     .481968          0          1

{txt}{hline}
-> country = Spain

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}      1,151    43.19722    13.55678         18         90
{txt}{space 6}female {c |}{res}      1,150    .5147826    .4999989          0          1
{txt}{space 9}edu {c |}{res}      1,145    15.70044    5.547022          0         60
{txt}{space 3}workingFT {c |}{res}      1,150    .4365217    .4961699          0          1

{txt}{hline}
-> country = Turkey FtoF

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        604    37.55464    13.08001         18         69
{txt}{space 6}female {c |}{res}        604          .5    .5004144          0          1
{txt}{space 9}edu {c |}{res}        601    9.058236    4.187474          0         22
{txt}{space 3}workingFT {c |}{res}        604    .4221854    .4943172          0          1

{txt}{hline}
-> country = Turkey online

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        951    31.95899    8.655233         18         66
{txt}{space 6}female {c |}{res}        947    .4899683    .5001635          0          1
{txt}{space 9}edu {c |}{res}        947    14.81626     3.39942          2         35
{txt}{space 3}workingFT {c |}{res}        946    .5761099    .4944347          0          1

{txt}{hline}
-> country = UK

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        992    36.68347    12.63628         18         76
{txt}{space 6}female {c |}{res}        988    .4949393    .5002276          0          1
{txt}{space 9}edu {c |}{res}        988    13.65283    4.686518          0         35
{txt}{space 3}workingFT {c |}{res}        990    .5040404    .5002364          0          1

{txt}{hline}
-> country = US

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}      1,145    45.36856    16.51997         18        100
{txt}{space 6}female {c |}{res}      1,145     .510917    .5000992          0          1
{txt}{space 9}edu {c |}{res}      1,145     13.8793    3.853531          0         34
{txt}{space 3}workingFT {c |}{res}      1,145    .4148472    .4929109          0          1

{txt}
{com}. 
. ****************************************
. ******GENERATE DATA FOR TABLE 1*********
. ****************************************
. 
. *********************************
. ***EMOTIONS MANIPULATION CHECK***
. ***SEE APPENDIX & FOOTNOTE 4*****
. *********************************
. 
. *perform factor analysis - reported on in body of manuscript
. 
. factor afraid anxious worried enthusiastic hopeful proud hatred contempt bitterness resentful, pcf
{txt}(obs=7,594)

Factor analysis/correlation{col 50}Number of obs    = {res}     7,594
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      27

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      3.94205      1.87548            0.3942       0.3942
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      2.06657      0.99042            0.2067       0.6009
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      1.07615      0.52771            0.1076       0.7085
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.54844      0.06316            0.0548       0.7633
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.48528      0.04843            0.0485       0.8118
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}      0.43685      0.02722            0.0437       0.8555
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}      0.40963      0.05170            0.0410       0.8965
{txt}{col 5}{ralign 11:Factor8}  {c |}{res}      0.35792      0.01815            0.0358       0.9323
{txt}{col 5}{ralign 11:Factor9}  {c |}{res}      0.33977      0.00243            0.0340       0.9663
{txt}{col 5}{ralign 11:Factor10}  {c |}{res}      0.33734            .            0.0337       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}45{txt}) ={res} 3.1e+04{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:afraid}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7474}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0381}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4271}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2576}}}{space 1}
{space 4}{space 0}{ralign 12:anxious}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7626}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0563}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3947}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2594}}}{space 1}
{space 4}{space 0}{ralign 12:worried}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7231}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0416}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5087}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2167}}}{space 1}
{space 4}{space 0}{ralign 12:enthusiastic}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0076}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8405}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1012}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2832}}}{space 1}
{space 4}{space 0}{ralign 12:hopeful}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.1058}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8481}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0260}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2688}}}{space 1}
{space 4}{space 0}{ralign 12:proud}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.0509}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.7866}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0519}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3760}}}{space 1}
{space 4}{space 0}{ralign 12:hatred}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7480}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0245}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3756}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2988}}}{space 1}
{space 4}{space 0}{ralign 12:contempt}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6899}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1002}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.4426}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3181}}}{space 1}
{space 4}{space 0}{ralign 12:bitterness}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8044}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0581}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2337}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2950}}}{space 1}
{space 4}{space 0}{ralign 12:resentful}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7634}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0406}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2720}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3416}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. rotate

{txt}Factor analysis/correlation{col 50}Number of obs    = {res}     7,594
{col 5}{txt}Method: principal-component factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: orthogonal varimax (Kaiser off){col 50}Number of params =   {res}      27

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Variance}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.62202      0.22490            0.2622       0.2622
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      2.39712      0.33149            0.2397       0.5019
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      2.06563            .            0.2066       0.7085
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}45{txt}) ={res} 3.1e+04{txt} Prob>chi2 ={res} 0.0000

{txt}Rotated factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:afraid}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2573}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8215}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0366}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2576}}}{space 1}
{space 4}{space 0}{ralign 12:anxious}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.2949}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8065}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0558}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2594}}}{space 1}
{space 4}{space 0}{ralign 12:worried}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1840}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8650}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0358}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2167}}}{space 1}
{space 4}{space 0}{ralign 12:enthusiastic}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0339}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0550}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8442}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2832}}}{space 1}
{space 4}{space 0}{ralign 12:hopeful}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0547}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0671}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8507}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2688}}}{space 1}
{space 4}{space 0}{ralign 12:proud}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.1102}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0168}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.7820}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3760}}}{space 1}
{space 4}{space 0}{ralign 12:hatred}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8047}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2316}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0101}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2988}}}{space 1}
{space 4}{space 0}{ralign 12:contempt}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8110}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1417}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0639}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3181}}}{space 1}
{space 4}{space 0}{ralign 12:bitterness}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7459}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3755}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0876}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2950}}}{space 1}
{space 4}{space 0}{ralign 12:resentful}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7465}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3179}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0102}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3416}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

Factor rotation matrix

{space 4}{hline 13}{c  TT}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 7:Factor1}{space 1}{space 1}{ralign 7:Factor2}{space 1}{space 1}{ralign 7:Factor3}{space 1}
{space 4}{hline 13}{c   +}{hline 9}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:Factor1}{space 1}{c |}{space 1}{ralign 7:{res:{sf: 0.7339}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.6788}}}{space 1}{space 1}{ralign 7:{res:{sf:-0.0237}}}{space 1}
{space 4}{space 0}{ralign 12:Factor2}{space 1}{c |}{space 1}{ralign 7:{res:{sf: 0.0479}}}{space 1}{space 1}{ralign 7:{res:{sf:-0.0169}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.9987}}}{space 1}
{space 4}{space 0}{ralign 12:Factor3}{space 1}{c |}{space 1}{ralign 7:{res:{sf:-0.6776}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.7341}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.0449}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 9}{hline 9}{hline 9}

{com}. predict anger anxiety enthusiasm
{txt}(regression scoring assumed)

{p 0 0 2}Scoring coefficients (method = regression; based on varimax rotated factors){p_end}

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:afraid}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.13065}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.42035}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.00511}}}{space 1}
{space 4}{space 0}{ralign 12:anxious}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.10526}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.40014}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.03906}}}{space 1}
{space 4}{space 0}{ralign 12:worried}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.18661}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.47183}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.00323}}}{space 1}
{space 4}{space 0}{ralign 12:enthusiastic}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.04567}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.06088}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.41047}}}{space 1}
{space 4}{space 0}{ralign 12:hopeful}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.01640}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.00744}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.41159}}}{space 1}
{space 4}{space 0}{ralign 12:proud}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.06039}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.03306}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.37766}}}{space 1}
{space 4}{space 0}{ralign 12:hatred}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.37632}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.12761}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.00831}}}{space 1}
{space 4}{space 0}{ralign 12:contempt}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.40944}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.18395}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.02583}}}{space 1}
{space 4}{space 0}{ralign 12:bitterness}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.29552}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.02040}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.04267}}}{space 1}
{space 4}{space 0}{ralign 12:resentful}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.31431}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.05441}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.00367}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}


{com}. 
. *create dummy to capture only control, good times, and the two international terror threat treatments
. recode treatment 1/4=1 5/8=0, gen(core_treatments)
{txt}(6262 differences between treatment and core_treatments)

{com}. 
. *The below generates the data for Appendix Table 2 (see also Fn4)
. reg anger gt tt tt_rbi if core_treatments==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     5,701
{txt}{hline 13}{c +}{hline 34}   F(3, 5697)      = {res}    19.55
{txt}       Model {c |} {res} 57.4233539         3   19.141118   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 5576.49238     5,697  .978847179   {txt}R-squared       ={res}    0.0102
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0097
{txt}       Total {c |} {res} 5633.91573     5,700  .988406268   {txt}Root MSE        =   {res} .98937

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       anger{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}gt {c |}{col 14}{res}{space 2} .0418978{col 26}{space 2} .0370309{col 37}{space 1}    1.13{col 46}{space 3}0.258{col 54}{space 4}-.0306969{col 67}{space 3} .1144925
{txt}{space 10}tt {c |}{col 14}{res}{space 2} .2365703{col 26}{space 2} .0367632{col 37}{space 1}    6.43{col 46}{space 3}0.000{col 54}{space 4} .1645004{col 67}{space 3} .3086403
{txt}{space 6}tt_rbi {c |}{col 14}{res}{space 2} .1971128{col 26}{space 2} .0370713{col 37}{space 1}    5.32{col 46}{space 3}0.000{col 54}{space 4}  .124439{col 67}{space 3} .2697865
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.1335288{col 26}{space 2} .0259731{col 37}{space 1}   -5.14{col 46}{space 3}0.000{col 54}{space 4} -.184446{col 67}{space 3}-.0826117
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. test tt=gt

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- gt + tt = 0{p_end}

{txt}       F(  1,  5697) ={res}   27.59
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. test tt_rbi=gt

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- gt + tt_rbi = 0{p_end}

{txt}       F(  1,  5697) ={res}   17.25
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. 
. reg enthusiasm gt tt tt_rbi if core_treatments==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     5,701
{txt}{hline 13}{c +}{hline 34}   F(3, 5697)      = {res}    15.52
{txt}       Model {c |} {res} 45.1978415         3  15.0659472   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 5530.59039     5,697  .970789958   {txt}R-squared       ={res}    0.0081
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0076
{txt}       Total {c |} {res} 5575.78823     5,700  .978208462   {txt}Root MSE        =   {res} .98529

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  enthusiasm{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}gt {c |}{col 14}{res}{space 2} -.020039{col 26}{space 2} .0368782{col 37}{space 1}   -0.54{col 46}{space 3}0.587{col 54}{space 4}-.0923343{col 67}{space 3} .0522563
{txt}{space 10}tt {c |}{col 14}{res}{space 2}-.1889775{col 26}{space 2} .0366116{col 37}{space 1}   -5.16{col 46}{space 3}0.000{col 54}{space 4}-.2607502{col 67}{space 3}-.1172048
{txt}{space 6}tt_rbi {c |}{col 14}{res}{space 2}-.1856817{col 26}{space 2} .0369184{col 37}{space 1}   -5.03{col 46}{space 3}0.000{col 54}{space 4}-.2580558{col 67}{space 3}-.1133077
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .1468463{col 26}{space 2}  .025866{col 37}{space 1}    5.68{col 46}{space 3}0.000{col 54}{space 4} .0961392{col 67}{space 3} .1975535
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. test tt=gt

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- gt + tt = 0{p_end}

{txt}       F(  1,  5697) ={res}   20.95
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. test tt_rbi=gt

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- gt + tt_rbi = 0{p_end}

{txt}       F(  1,  5697) ={res}   19.81
{txt}{col 13}Prob > F ={res}    0.0000
{txt}
{com}. 
. reg anxiety gt tt tt_rbi if core_treatments==1

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}     5,701
{txt}{hline 13}{c +}{hline 34}   F(3, 5697)      = {res}     1.43
{txt}       Model {c |} {res} 4.19572169         3   1.3985739   {txt}Prob > F        ={res}    0.2325
{txt}    Residual {c |} {res} 5580.09086     5,697  .979478824   {txt}R-squared       ={res}    0.0008
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0002
{txt}       Total {c |} {res} 5584.28658     5,700    .9796994   {txt}Root MSE        =   {res} .98969

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}     anxiety{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}gt {c |}{col 14}{res}{space 2}-.0120014{col 26}{space 2} .0370429{col 37}{space 1}   -0.32{col 46}{space 3}0.746{col 54}{space 4}-.0846196{col 67}{space 3} .0606167
{txt}{space 10}tt {c |}{col 14}{res}{space 2}-.0391877{col 26}{space 2} .0367751{col 37}{space 1}   -1.07{col 46}{space 3}0.287{col 54}{space 4}-.1112809{col 67}{space 3} .0329055
{txt}{space 6}tt_rbi {c |}{col 14}{res}{space 2} .0364776{col 26}{space 2} .0370832{col 37}{space 1}    0.98{col 46}{space 3}0.325{col 54}{space 4}-.0362196{col 67}{space 3} .1091748
{txt}{space 7}_cons {c |}{col 14}{res}{space 2}-.0339082{col 26}{space 2} .0259815{col 37}{space 1}   -1.31{col 46}{space 3}0.192{col 54}{space 4}-.0848418{col 67}{space 3} .0170254
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. test tt=gt

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- gt + tt = 0{p_end}

{txt}       F(  1,  5697) ={res}    0.54
{txt}{col 13}Prob > F ={res}    0.4634
{txt}
{com}. test tt_rbi=gt

{p 0 7}{space 1}{text:( 1)}{space 1}{space 1}{res}- gt + tt_rbi = 0{p_end}

{txt}       F(  1,  5697) ={res}    1.68
{txt}{col 13}Prob > F ={res}    0.1947
{txt}
{com}. 
. 
. *********************************
. *********BALANCE CHECK***********
. ***SEE APPENDIX & FOOTNOTE 7*****
. *********************************
. 
. *The below generates the data for Appendix Table 3 (see also Fn7)
. sort country
{txt}
{com}. by country: anova age treatment

{txt}{hline}
-> country = Albania

                         Number of obs = {res}       600    {txt}R-squared     ={res}  0.0098
                         {txt}Root MSE      =   {res} 13.9624    {txt}Adj R-squared ={res}  0.0032

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res}  1151.076          4   287.76899      1.48  0.2079
                         {txt}{c |}
               treatment {c |} {res}  1151.076          4   287.76899      1.48  0.2079
                         {txt}{c |}
                Residual {c |} {res} 115994.12        595    194.9481  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 117145.19        599   195.56794  

{txt}{hline}
-> country = Ecuador

                         Number of obs = {res}       600    {txt}R-squared     ={res}  0.0016
                         {txt}Root MSE      =   {res} 13.8587    {txt}Adj R-squared ={res} -0.0068

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 181.76528          5   36.353056      0.19  0.9666
                         {txt}{c |}
               treatment {c |} {res} 181.76528          5   36.353056      0.19  0.9666
                         {txt}{c |}
                Residual {c |} {res} 114086.07        594   192.06409  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 114267.83        599   190.76433  

{txt}{hline}
-> country = France

                         Number of obs = {res}       943    {txt}R-squared     ={res}  0.0075
                         {txt}Root MSE      =   {res} 14.3946    {txt}Adj R-squared ={res}  0.0033

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 1473.8833          4   368.47082      1.78  0.1310
                         {txt}{c |}
               treatment {c |} {res} 1473.8833          4   368.47082      1.78  0.1310
                         {txt}{c |}
                Residual {c |} {res} 194358.38        938   207.20509  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 195832.26        942   207.88987  

{txt}{hline}
-> country = Peru

                         Number of obs = {res}       760    {txt}R-squared     ={res}  0.0029
                         {txt}Root MSE      =   {res} 16.0821    {txt}Adj R-squared ={res} -0.0024

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 571.09031          4   142.77258      0.55  0.6976
                         {txt}{c |}
               treatment {c |} {res} 571.09031          4   142.77258      0.55  0.6976
                         {txt}{c |}
                Residual {c |} {res} 195269.75        755   258.63543  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 195840.84        759   258.02482  

{txt}{hline}
-> country = Spain

                         Number of obs = {res}     1,150    {txt}R-squared     ={res}  0.0046
                         {txt}Root MSE      =   {res} 13.5608    {txt}Adj R-squared ={res}  0.0003

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 977.40335          5   195.48067      1.06  0.3793
                         {txt}{c |}
               treatment {c |} {res} 977.40335          5   195.48067      1.06  0.3793
                         {txt}{c |}
                Residual {c |} {res} 210375.39      1,144   183.89457  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res}  211352.8      1,149   183.94499  

{txt}{hline}
-> country = Turkey FtoF

                         Number of obs = {res}       604    {txt}R-squared     ={res}  0.0098
                         {txt}Root MSE      =   {res} 13.0594    {txt}Adj R-squared ={res}  0.0032

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res}   1007.47          4   251.86751      1.48  0.2077
                         {txt}{c |}
               treatment {c |} {res}   1007.47          4   251.86751      1.48  0.2077
                         {txt}{c |}
                Residual {c |} {res} 102157.73        599   170.54712  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res}  103165.2        603   171.08656  

{txt}{hline}
-> country = Turkey online

                         Number of obs = {res}       947    {txt}R-squared     ={res}  0.0067
                         {txt}Root MSE      =   {res} 8.64211    {txt}Adj R-squared ={res}  0.0025

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 472.27384          4   118.06846      1.58  0.1772
                         {txt}{c |}
               treatment {c |} {res} 472.27384          4   118.06846      1.58  0.1772
                         {txt}{c |}
                Residual {c |} {res} 70354.265        942   74.686056  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 70826.539        946   74.869491  

{txt}{hline}
-> country = UK

                         Number of obs = {res}       990    {txt}R-squared     ={res}  0.0009
                         {txt}Root MSE      =   {res}  12.665    {txt}Adj R-squared ={res} -0.0031

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 147.65297          4   36.913242      0.23  0.9215
                         {txt}{c |}
               treatment {c |} {res} 147.65297          4   36.913242      0.23  0.9215
                         {txt}{c |}
                Residual {c |} {res} 157996.61        985   160.40265  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 158144.26        989    159.9032  

{txt}{hline}
-> country = US

                         Number of obs = {res}     1,145    {txt}R-squared     ={res}  0.0014
                         {txt}Root MSE      =   {res} 16.5447    {txt}Adj R-squared ={res} -0.0030

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 433.72433          5   86.744865      0.32  0.9030
                         {txt}{c |}
               treatment {c |} {res} 433.72433          5   86.744865      0.32  0.9030
                         {txt}{c |}
                Residual {c |} {res} 311774.74      1,139   273.72673  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 312208.47      1,144    272.9095  
{txt}
{com}. by country: tabulate female treatment, chi

{txt}{hline}
-> country = Albania

 RECODE of {c |}
    gender {c |}                       Treatment
  (Gender) {c |}   Control  Good Time  Terror (n  Terror (w  Economic  {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}        45         48         57         40         38 {txt}{c |}{res}       228 
{txt}         1 {c |}{res}        78         73         66         76         79 {txt}{c |}{res}       372 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       123        121        123        116        117 {txt}{c |}{res}       600 

{txt}          Pearson chi2({res}4{txt}) = {res}  6.0032  {txt} Pr = {res}0.199

{txt}{hline}
-> country = Ecuador

 RECODE of {c |}
    gender {c |}                             Treatment
  (Gender) {c |}   Control  Good Time  Terror (n  Terror (w  Crime Thr  Domestic  {c |}     Total
{hline 11}{c +}{hline 66}{c +}{hline 10}
         0 {c |}{res}        50         46         51         50         44         54 {txt}{c |}{res}       295 
{txt}         1 {c |}{res}        50         55         50         47         57         46 {txt}{c |}{res}       305 
{txt}{hline 11}{c +}{hline 66}{c +}{hline 10}
     Total {c |}{res}       100        101        101         97        101        100 {txt}{c |}{res}       600 

{txt}          Pearson chi2({res}5{txt}) = {res}  3.0521  {txt} Pr = {res}0.692

{txt}{hline}
-> country = France

 RECODE of {c |}
    gender {c |}                       Treatment
  (Gender) {c |}   Control  Good Time  Terror (n  Terror (w  Economic  {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}        81         81         96         83         95 {txt}{c |}{res}       436 
{txt}         1 {c |}{res}       112        102         88        111         93 {txt}{c |}{res}       506 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       193        183        184        194        188 {txt}{c |}{res}       942 

{txt}          Pearson chi2({res}4{txt}) = {res}  6.6344  {txt} Pr = {res}0.157

{txt}{hline}
-> country = Peru

 RECODE of {c |}
    gender {c |}                       Treatment
  (Gender) {c |}   Control  Good Time  Terror (n  Terror (w  Domestic  {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}        77         68         77         76         80 {txt}{c |}{res}       378 
{txt}         1 {c |}{res}        74         85         73         75         75 {txt}{c |}{res}       382 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       151        153        150        151        155 {txt}{c |}{res}       760 

{txt}          Pearson chi2({res}4{txt}) = {res}  2.2021  {txt} Pr = {res}0.699

{txt}{hline}
-> country = Spain

 RECODE of {c |}
    gender {c |}                             Treatment
  (Gender) {c |}   Control  Good Time  Terror (n  Terror (w  Economic   Domestic  {c |}     Total
{hline 11}{c +}{hline 66}{c +}{hline 10}
         0 {c |}{res}        98         96        103         82         94         85 {txt}{c |}{res}       558 
{txt}         1 {c |}{res}       104         98         90         99         94        107 {txt}{c |}{res}       592 
{txt}{hline 11}{c +}{hline 66}{c +}{hline 10}
     Total {c |}{res}       202        194        193        181        188        192 {txt}{c |}{res}     1,150 

{txt}          Pearson chi2({res}5{txt}) = {res}  4.1904  {txt} Pr = {res}0.522

{txt}{hline}
-> country = Turkey FtoF

 RECODE of {c |}
    gender {c |}                       Treatment
  (Gender) {c |}   Control  Good Time  Terror (n  Terror (w  Domestic  {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}        68         65         58         56         55 {txt}{c |}{res}       302 
{txt}         1 {c |}{res}        58         52         66         69         57 {txt}{c |}{res}       302 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       126        117        124        125        112 {txt}{c |}{res}       604 

{txt}          Pearson chi2({res}4{txt}) = {res}  4.1419  {txt} Pr = {res}0.387

{txt}{hline}
-> country = Turkey online

 RECODE of {c |}
    gender {c |}                       Treatment
  (Gender) {c |}   Control  Good Time  Terror (n  Terror (w  Economic  {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}       101         95        110         91         86 {txt}{c |}{res}       483 
{txt}         1 {c |}{res}        91         90         93        100         90 {txt}{c |}{res}       464 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       192        185        203        191        176 {txt}{c |}{res}       947 

{txt}          Pearson chi2({res}4{txt}) = {res}  2.2143  {txt} Pr = {res}0.696

{txt}{hline}
-> country = UK

 RECODE of {c |}
    gender {c |}                       Treatment
  (Gender) {c |}   Control  Good Time  Terror (n  Terror (w  Economic  {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}       105         93        105        102         94 {txt}{c |}{res}       499 
{txt}         1 {c |}{res}        91         97         93         98        110 {txt}{c |}{res}       489 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       196        190        198        200        204 {txt}{c |}{res}       988 

{txt}          Pearson chi2({res}4{txt}) = {res}  3.0455  {txt} Pr = {res}0.550

{txt}{hline}
-> country = US

 RECODE of {c |}
    gender {c |}                             Treatment
  (Gender) {c |}   Control  Good Time  Terror (n  Terror (w  Economic   Terror (w {c |}     Total
{hline 11}{c +}{hline 66}{c +}{hline 10}
         0 {c |}{res}        91         85         98         95         99         92 {txt}{c |}{res}       560 
{txt}         1 {c |}{res}       103        101        103         82         97         99 {txt}{c |}{res}       585 
{txt}{hline 11}{c +}{hline 66}{c +}{hline 10}
     Total {c |}{res}       194        186        201        177        196        191 {txt}{c |}{res}     1,145 

{txt}          Pearson chi2({res}5{txt}) = {res}  2.9303  {txt} Pr = {res}0.711

{txt}
{com}. by country: anova edu treatment

{txt}{hline}
-> country = Albania

                         Number of obs = {res}       599    {txt}R-squared     ={res}  0.0041
                         {txt}Root MSE      =   {res} 3.18638    {txt}Adj R-squared ={res} -0.0026

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 24.708815          4   6.1772038      0.61  0.6567
                         {txt}{c |}
               treatment {c |} {res} 24.708815          4   6.1772038      0.61  0.6567
                         {txt}{c |}
                Residual {c |} {res} 6030.9005        594   10.153031  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 6055.6093        598   10.126437  

{txt}{hline}
-> country = Ecuador

                         Number of obs = {res}       600    {txt}R-squared     ={res}  0.0040
                         {txt}Root MSE      =   {res} 3.96459    {txt}Adj R-squared ={res} -0.0044

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 37.210173          5   7.4420346      0.47  0.7961
                         {txt}{c |}
               treatment {c |} {res} 37.210173          5   7.4420346      0.47  0.7961
                         {txt}{c |}
                Residual {c |} {res} 9336.4632        594   15.717951  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 9373.6733        599    15.64887  

{txt}{hline}
-> country = France

                         Number of obs = {res}       942    {txt}R-squared     ={res}  0.0109
                         {txt}Root MSE      =   {res} 4.06538    {txt}Adj R-squared ={res}  0.0067

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 170.83311          4   42.708278      2.58  0.0358
                         {txt}{c |}
               treatment {c |} {res} 170.83311          4   42.708278      2.58  0.0358
                         {txt}{c |}
                Residual {c |} {res} 15486.118        937   16.527341  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 15656.951        941    16.63863  

{txt}{hline}
-> country = Peru

                         Number of obs = {res}       758    {txt}R-squared     ={res}  0.0019
                         {txt}Root MSE      =   {res} 3.60991    {txt}Adj R-squared ={res} -0.0034

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 19.118241          4   4.7795602      0.37  0.8324
                         {txt}{c |}
               treatment {c |} {res} 19.118241          4   4.7795602      0.37  0.8324
                         {txt}{c |}
                Residual {c |} {res} 9812.7076        753   13.031484  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 9831.8259        757   12.987881  

{txt}{hline}
-> country = Spain

                         Number of obs = {res}     1,145    {txt}R-squared     ={res}  0.0019
                         {txt}Root MSE      =   {res} 5.55401    {txt}Adj R-squared ={res} -0.0025

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 65.517454          5   13.103491      0.42  0.8316
                         {txt}{c |}
               treatment {c |} {res} 65.517454          5   13.103491      0.42  0.8316
                         {txt}{c |}
                Residual {c |} {res} 35134.732      1,139   30.846999  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res}  35200.25      1,144   30.769449  

{txt}{hline}
-> country = Turkey FtoF

                         Number of obs = {res}       601    {txt}R-squared     ={res}  0.0061
                         {txt}Root MSE      =   {res} 4.18877    {txt}Adj R-squared ={res} -0.0006

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 63.660775          4   15.915194      0.91  0.4594
                         {txt}{c |}
               treatment {c |} {res} 63.660775          4   15.915194      0.91  0.4594
                         {txt}{c |}
                Residual {c |} {res} 10457.301        596   17.545807  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 10520.962        600   17.534936  

{txt}{hline}
-> country = Turkey online

                         Number of obs = {res}       947    {txt}R-squared     ={res}  0.0014
                         {txt}Root MSE      =   {res} 3.40432    {txt}Adj R-squared ={res} -0.0029

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 14.849633          4   3.7124083      0.32  0.8645
                         {txt}{c |}
               treatment {c |} {res} 14.849633          4   3.7124083      0.32  0.8645
                         {txt}{c |}
                Residual {c |} {res}  10917.18        942   11.589363  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res}  10932.03        946   11.556057  

{txt}{hline}
-> country = UK

                         Number of obs = {res}       988    {txt}R-squared     ={res}  0.0061
                         {txt}Root MSE      =   {res} 4.68179    {txt}Adj R-squared ={res}  0.0020

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 131.41386          4   32.853464      1.50  0.2004
                         {txt}{c |}
               treatment {c |} {res} 131.41386          4   32.853464      1.50  0.2004
                         {txt}{c |}
                Residual {c |} {res} 21546.508        983   21.919133  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 21677.922        987   21.963447  

{txt}{hline}
-> country = US

                         Number of obs = {res}     1,145    {txt}R-squared     ={res}  0.0031
                         {txt}Root MSE      =   {res} 3.85607    {txt}Adj R-squared ={res} -0.0013

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
              {hline 11}{c +}{hline 52}
                   Model {c |} {res} 51.917468          5   10.383494      0.70  0.6248
                         {txt}{c |}
               treatment {c |} {res} 51.917468          5   10.383494      0.70  0.6248
                         {txt}{c |}
                Residual {c |} {res} 16936.142      1,139   14.869308  
              {txt}{hline 11}{c +}{hline 52}
                   Total {c |} {res} 16988.059      1,144   14.849702  
{txt}
{com}. by country: tabulate workingFT treatment, chi

{txt}{hline}
-> country = Albania

 RECODE of {c |}
ocup (What {c |}
      best {c |}
 describes {c |}
      your {c |}
   current {c |}
employment {c |}                       Treatment
  status?) {c |}   Control  Good Time  Terror (n  Terror (w  Economic  {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}        64         77         63         64         68 {txt}{c |}{res}       336 
{txt}         1 {c |}{res}        59         44         59         50         49 {txt}{c |}{res}       261 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       123        121        122        114        117 {txt}{c |}{res}       597 

{txt}          Pearson chi2({res}4{txt}) = {res}  4.7927  {txt} Pr = {res}0.309

{txt}{hline}
-> country = Ecuador

 RECODE of {c |}
ocup (What {c |}
      best {c |}
 describes {c |}
      your {c |}
   current {c |}
employment {c |}                             Treatment
  status?) {c |}   Control  Good Time  Terror (n  Terror (w  Crime Thr  Domestic  {c |}     Total
{hline 11}{c +}{hline 66}{c +}{hline 10}
         0 {c |}{res}        49         53         51         46         50         51 {txt}{c |}{res}       300 
{txt}         1 {c |}{res}        51         48         50         51         51         49 {txt}{c |}{res}       300 
{txt}{hline 11}{c +}{hline 66}{c +}{hline 10}
     Total {c |}{res}       100        101        101         97        101        100 {txt}{c |}{res}       600 

{txt}          Pearson chi2({res}5{txt}) = {res}  0.6051  {txt} Pr = {res}0.988

{txt}{hline}
-> country = France

 RECODE of {c |}
ocup (What {c |}
      best {c |}
 describes {c |}
      your {c |}
   current {c |}
employment {c |}                       Treatment
  status?) {c |}   Control  Good Time  Terror (n  Terror (w  Economic  {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}        98         98        100        103        106 {txt}{c |}{res}       505 
{txt}         1 {c |}{res}        95         85         84         92         82 {txt}{c |}{res}       438 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       193        183        184        195        188 {txt}{c |}{res}       943 

{txt}          Pearson chi2({res}4{txt}) = {res}  1.2920  {txt} Pr = {res}0.863

{txt}{hline}
-> country = Peru

 RECODE of {c |}
ocup (What {c |}
      best {c |}
 describes {c |}
      your {c |}
   current {c |}
employment {c |}                       Treatment
  status?) {c |}   Control  Good Time  Terror (n  Terror (w  Domestic  {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}        92        102         94         90        104 {txt}{c |}{res}       482 
{txt}         1 {c |}{res}        59         51         56         61         51 {txt}{c |}{res}       278 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       151        153        150        151        155 {txt}{c |}{res}       760 

{txt}          Pearson chi2({res}4{txt}) = {res}  2.9881  {txt} Pr = {res}0.560

{txt}{hline}
-> country = Spain

 RECODE of {c |}
ocup (What {c |}
      best {c |}
 describes {c |}
      your {c |}
   current {c |}
employment {c |}                             Treatment
  status?) {c |}   Control  Good Time  Terror (n  Terror (w  Economic   Domestic  {c |}     Total
{hline 11}{c +}{hline 66}{c +}{hline 10}
         0 {c |}{res}       110        111        115        109         91        112 {txt}{c |}{res}       648 
{txt}         1 {c |}{res}        92         83         78         72         97         80 {txt}{c |}{res}       502 
{txt}{hline 11}{c +}{hline 66}{c +}{hline 10}
     Total {c |}{res}       202        194        193        181        188        192 {txt}{c |}{res}     1,150 

{txt}          Pearson chi2({res}5{txt}) = {res}  7.4106  {txt} Pr = {res}0.192

{txt}{hline}
-> country = Turkey FtoF

 RECODE of {c |}
ocup (What {c |}
      best {c |}
 describes {c |}
      your {c |}
   current {c |}
employment {c |}                       Treatment
  status?) {c |}   Control  Good Time  Terror (n  Terror (w  Domestic  {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}        74         66         74         68         67 {txt}{c |}{res}       349 
{txt}         1 {c |}{res}        52         51         50         57         45 {txt}{c |}{res}       255 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       126        117        124        125        112 {txt}{c |}{res}       604 

{txt}          Pearson chi2({res}4{txt}) = {res}  1.0964  {txt} Pr = {res}0.895

{txt}{hline}
-> country = Turkey online

 RECODE of {c |}
ocup (What {c |}
      best {c |}
 describes {c |}
      your {c |}
   current {c |}
employment {c |}                       Treatment
  status?) {c |}   Control  Good Time  Terror (n  Terror (w  Economic  {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}        88         74         85         88         66 {txt}{c |}{res}       401 
{txt}         1 {c |}{res}       104        111        118        103        109 {txt}{c |}{res}       545 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       192        185        203        191        175 {txt}{c |}{res}       946 

{txt}          Pearson chi2({res}4{txt}) = {res}  4.0150  {txt} Pr = {res}0.404

{txt}{hline}
-> country = UK

 RECODE of {c |}
ocup (What {c |}
      best {c |}
 describes {c |}
      your {c |}
   current {c |}
employment {c |}                       Treatment
  status?) {c |}   Control  Good Time  Terror (n  Terror (w  Economic  {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
         0 {c |}{res}        91         96         90        104        110 {txt}{c |}{res}       491 
{txt}         1 {c |}{res}       105         94        110         96         94 {txt}{c |}{res}       499 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       196        190        200        200        204 {txt}{c |}{res}       990 

{txt}          Pearson chi2({res}4{txt}) = {res}  4.5316  {txt} Pr = {res}0.339

{txt}{hline}
-> country = US

 RECODE of {c |}
ocup (What {c |}
      best {c |}
 describes {c |}
      your {c |}
   current {c |}
employment {c |}                             Treatment
  status?) {c |}   Control  Good Time  Terror (n  Terror (w  Economic   Terror (w {c |}     Total
{hline 11}{c +}{hline 66}{c +}{hline 10}
         0 {c |}{res}       113        119        114         95        122        107 {txt}{c |}{res}       670 
{txt}         1 {c |}{res}        81         67         87         82         74         84 {txt}{c |}{res}       475 
{txt}{hline 11}{c +}{hline 66}{c +}{hline 10}
     Total {c |}{res}       194        186        201        177        196        191 {txt}{c |}{res}     1,145 

{txt}          Pearson chi2({res}5{txt}) = {res}  5.8834  {txt} Pr = {res}0.318

{txt}
{com}. 
. 
. *********************************
. ***********WORRY CHECK***********
. ***SEE APPENDIX & FOOTNOTE 8*****
. *********************************
. 
. *The below generates the data for Appendix Table 4 (see also Fn8)
. recode emo2 1/2=0 666=0 3/4=1  *=., gen(vworried)
{txt}(7726 differences between emo2 and vworried)

{com}. 
. sort country
{txt}
{com}. by country: tab vworried if control==1

{txt}{hline}
-> country = Albania

  RECODE of {c |}
  emo2 (How {c |}
worried are {c |}
   you that {c |}
 there will {c |}
       be a {c |}
    violent {c |}
  attack by {c |}
      terro {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         73       62.93       62.93
{txt}          1 {c |}{res}         43       37.07      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        116      100.00

{txt}{hline}
-> country = Ecuador

  RECODE of {c |}
  emo2 (How {c |}
worried are {c |}
   you that {c |}
 there will {c |}
       be a {c |}
    violent {c |}
  attack by {c |}
      terro {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         55       55.56       55.56
{txt}          1 {c |}{res}         44       44.44      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}         99      100.00

{txt}{hline}
-> country = France

  RECODE of {c |}
  emo2 (How {c |}
worried are {c |}
   you that {c |}
 there will {c |}
       be a {c |}
    violent {c |}
  attack by {c |}
      terro {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        122       63.21       63.21
{txt}          1 {c |}{res}         71       36.79      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        193      100.00

{txt}{hline}
-> country = Peru

  RECODE of {c |}
  emo2 (How {c |}
worried are {c |}
   you that {c |}
 there will {c |}
       be a {c |}
    violent {c |}
  attack by {c |}
      terro {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         51       34.93       34.93
{txt}          1 {c |}{res}         95       65.07      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        146      100.00

{txt}{hline}
-> country = Spain

  RECODE of {c |}
  emo2 (How {c |}
worried are {c |}
   you that {c |}
 there will {c |}
       be a {c |}
    violent {c |}
  attack by {c |}
      terro {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        109       53.96       53.96
{txt}          1 {c |}{res}         93       46.04      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        202      100.00

{txt}{hline}
-> country = Turkey FtoF

  RECODE of {c |}
  emo2 (How {c |}
worried are {c |}
   you that {c |}
 there will {c |}
       be a {c |}
    violent {c |}
  attack by {c |}
      terro {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         32       27.35       27.35
{txt}          1 {c |}{res}         85       72.65      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        117      100.00

{txt}{hline}
-> country = Turkey online

  RECODE of {c |}
  emo2 (How {c |}
worried are {c |}
   you that {c |}
 there will {c |}
       be a {c |}
    violent {c |}
  attack by {c |}
      terro {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         34       17.71       17.71
{txt}          1 {c |}{res}        158       82.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        192      100.00

{txt}{hline}
-> country = UK

  RECODE of {c |}
  emo2 (How {c |}
worried are {c |}
   you that {c |}
 there will {c |}
       be a {c |}
    violent {c |}
  attack by {c |}
      terro {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        129       65.82       65.82
{txt}          1 {c |}{res}         67       34.18      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        196      100.00

{txt}{hline}
-> country = US

  RECODE of {c |}
  emo2 (How {c |}
worried are {c |}
   you that {c |}
 there will {c |}
       be a {c |}
    violent {c |}
  attack by {c |}
      terro {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        112       57.73       57.73
{txt}          1 {c |}{res}         82       42.27      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        194      100.00

{txt}
{com}. 
. *********************************
. *******CREATE INFO MEASURES******
. ***********FOR FIGURE 1**********
. *********************************
. 
. *The below code for GOOD TIMES, INTL TT-No remidner, and INTL TT-Reminder generates the combined Information measures used to generate Figure 1.
. 
. *****GOOD TIMES (questions = mcn1 & mcn2)*****
. 
. *Coding information for mcn1 (Good Times Info Q1): 1 = more than half (correct answer); 2 = less than half (incorrect answer); 888/988/999=DK, DK/NR, NR
. 
. *code missing to 0 (incorrect); keep correct=1
. gen GTInfo1=mcn1
{txt}(6,339 missing values generated)

{com}. recode GTInfo1 1=1 2=0 888=0 988=0 999=0
{txt}(GTInfo1: 477 changes made)

{com}. recode GTInfo1 .=0 if gt==1
{txt}(GTInfo1: 23 changes made)

{com}. 
. *Coding information for mcn2 (Good Times Info Q2): 1 = improved (correct answer); 2 = deteriorated (incorrect answer); 888/988/999 as above
. 
. *code missing to 0 (incorrect); keep correct=1
. gen GTInfo2=mcn2
{txt}(6,338 missing values generated)

{com}. recode GTInfo2 1=1 2=0 888=0 988=0 999=0
{txt}(GTInfo2: 526 changes made)

{com}. recode GTInfo2 .=0 if gt==1
{txt}(GTInfo2: 22 changes made)

{com}. 
. *generate combined GT Info variable
. gen GTInfo=GTInfo1+GTInfo2
{txt}(6,316 missing values generated)

{com}. 
. *****INTL TT - No Reminder (questions = mct1a & mct2a)*****
. 
. *Coding information for mct1a(TT-NR): 1 = more than half (correct answer); 2 = less than half (incorrect answer); 888/999 as above
. 
. gen TTNRInfo1=mct1a
{txt}(6,285 missing values generated)

{com}. recode TTNRInfo1 1=1 2=0 888=0 988=0 999=0
{txt}(TTNRInfo1: 335 changes made)

{com}. recode TTNRInfo1 .=0 if tt==1
{txt}(TTNRInfo1: 18 changes made)

{com}. 
. *Coding information for mct2a(TT-NR): 1 = less than 100 (incorrect answer); 2 = more than 100 (correct answer); 888 same as above
. 
. gen TTNRInfo2=mct2a
{txt}(6,285 missing values generated)

{com}. recode TTNRInfo2 1=0 2=1 888=0 988=0 999=0
{txt}(TTNRInfo2: 1461 changes made)

{com}. recode TTNRInfo2 .=0 if tt==1
{txt}(TTNRInfo2: 18 changes made)

{com}. 
. *generate combined TTNR variable
. gen TTNRInfo=TTNRInfo1+TTNRInfo2
{txt}(6,267 missing values generated)

{com}. 
. *****INTL TT - Reminder (questions = mct1b & mct2b)*****
. 
. *Coding information for mct1b(TT-R): 1 = more than half (correct answer); 2 = less than half (incorrect answer); 888/988/999 as above
. 
. gen TTRInfo1=mct1b
{txt}(6,337 missing values generated)

{com}. recode TTRInfo1 1=1 2=0 888=0 988=0 999=0
{txt}(TTRInfo1: 333 changes made)

{com}. recode TTRInfo1 .=0 if tt_rbi==1
{txt}(TTRInfo1: 24 changes made)

{com}. 
. *Coding information for mct2b(TT-R): 1 = less than 100 (incorrect answer); 2 = more than 100 (correct answer); 888/998/999 same as above
. 
. gen TTRInfo2=mct2b
{txt}(6,336 missing values generated)

{com}. recode TTRInfo2 1=0 2=1 888=0 988=0 999=0
{txt}(TTRInfo2: 1410 changes made)

{com}. recode TTRInfo2 .=0 if tt_rbi==1
{txt}(TTRInfo2: 23 changes made)

{com}. 
. *generate combined varaible
. gen TTRInfo=TTRInfo1+TTRInfo2
{txt}(6,313 missing values generated)

{com}. 
. 
. *********************************
. *******GENERATE OUTPUT FOR*******
. ************ FIGURE 1 ***********
. *********************************
. 
. mean GTInfo, over(country)
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}     1,430

      {txt}Albania: country = {res}Albania
      {txt}Ecuador: country = {res}Ecuador
       {txt}France: country = {res}France
         {txt}Peru: country = {res}Peru
        {txt}Spain: country = {res}Spain
    {txt}_subpop_6: country = {res}Turkey FtoF
    {txt}_subpop_7: country = {res}Turkey online
           {txt}UK: country = {res}UK
           {txt}US: country = {res}US

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 1}        Over{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{res}GTInfo       {txt}{c |}
{space 5}Albania {c |}{col 14}{res}{space 2} .9752066{col 26}{space 2} .0833715{col 37}{space 5}  .811663{col 51}{space 3}  1.13875
{txt}{space 5}Ecuador {c |}{col 14}{res}{space 2} .8217822{col 26}{space 2} .0800941{col 37}{space 5} .6646676{col 51}{space 3} .9788967
{txt}{space 6}France {c |}{col 14}{res}{space 2} 1.338798{col 26}{space 2} .0602728{col 37}{space 5} 1.220565{col 51}{space 3} 1.457031
{txt}{space 8}Peru {c |}{col 14}{res}{space 2} .8169935{col 26}{space 2} .0658607{col 37}{space 5} .6877995{col 51}{space 3} .9461874
{txt}{space 7}Spain {c |}{col 14}{res}{space 2} 1.319588{col 26}{space 2}  .058966{col 37}{space 5} 1.203918{col 51}{space 3} 1.435257
{txt}{space 3}_subpop_6 {c |}{col 14}{res}{space 2} 1.145299{col 26}{space 2} .0779801{col 37}{space 5} .9923314{col 51}{space 3} 1.298267
{txt}{space 3}_subpop_7 {c |}{col 14}{res}{space 2} 1.508108{col 26}{space 2}  .052616{col 37}{space 5} 1.404895{col 51}{space 3} 1.611321
{txt}{space 10}UK {c |}{col 14}{res}{space 2} 1.405263{col 26}{space 2}  .055796{col 37}{space 5} 1.295812{col 51}{space 3} 1.514714
{txt}{space 10}US {c |}{col 14}{res}{space 2} 1.639785{col 26}{space 2} .0490732{col 37}{space 5} 1.543522{col 51}{space 3} 1.736048
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. mean TTNRInfo, over(country)
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}     1,479

      {txt}Albania: country = {res}Albania
      {txt}Ecuador: country = {res}Ecuador
       {txt}France: country = {res}France
         {txt}Peru: country = {res}Peru
        {txt}Spain: country = {res}Spain
    {txt}_subpop_6: country = {res}Turkey FtoF
    {txt}_subpop_7: country = {res}Turkey online
           {txt}UK: country = {res}UK
           {txt}US: country = {res}US

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 1}        Over{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{res}TTNRInfo     {txt}{c |}
{space 5}Albania {c |}{col 14}{res}{space 2} 1.495935{col 26}{space 2} .0614954{col 37}{space 5} 1.375307{col 51}{space 3} 1.616563
{txt}{space 5}Ecuador {c |}{col 14}{res}{space 2} 1.376238{col 26}{space 2}  .074264{col 37}{space 5} 1.230564{col 51}{space 3} 1.521912
{txt}{space 6}France {c |}{col 14}{res}{space 2} 1.744565{col 26}{space 2} .0348922{col 37}{space 5} 1.676122{col 51}{space 3} 1.813009
{txt}{space 8}Peru {c |}{col 14}{res}{space 2}     1.46{col 26}{space 2} .0602975{col 37}{space 5} 1.341722{col 51}{space 3} 1.578278
{txt}{space 7}Spain {c |}{col 14}{res}{space 2} 1.704663{col 26}{space 2} .0367938{col 37}{space 5}  1.63249{col 51}{space 3} 1.776837
{txt}{space 3}_subpop_6 {c |}{col 14}{res}{space 2} 1.354839{col 26}{space 2} .0707696{col 37}{space 5} 1.216019{col 51}{space 3} 1.493658
{txt}{space 3}_subpop_7 {c |}{col 14}{res}{space 2} 1.773399{col 26}{space 2} .0325988{col 37}{space 5} 1.709454{col 51}{space 3} 1.837344
{txt}{space 10}UK {c |}{col 14}{res}{space 2}    1.645{col 26}{space 2} .0412722{col 37}{space 5} 1.564042{col 51}{space 3} 1.725958
{txt}{space 10}US {c |}{col 14}{res}{space 2} 1.791045{col 26}{space 2} .0327906{col 37}{space 5} 1.726724{col 51}{space 3} 1.855366
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. mean TTRInfo, over(country)
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}     1,433

      {txt}Albania: country = {res}Albania
      {txt}Ecuador: country = {res}Ecuador
       {txt}France: country = {res}France
         {txt}Peru: country = {res}Peru
        {txt}Spain: country = {res}Spain
    {txt}_subpop_6: country = {res}Turkey FtoF
    {txt}_subpop_7: country = {res}Turkey online
           {txt}UK: country = {res}UK
           {txt}US: country = {res}US

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 1}        Over{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{res}TTRInfo      {txt}{c |}
{space 5}Albania {c |}{col 14}{res}{space 2} 1.353448{col 26}{space 2} .0662265{col 37}{space 5} 1.223537{col 51}{space 3}  1.48336
{txt}{space 5}Ecuador {c |}{col 14}{res}{space 2} 1.391753{col 26}{space 2} .0740877{col 37}{space 5} 1.246421{col 51}{space 3} 1.537085
{txt}{space 6}France {c |}{col 14}{res}{space 2} 1.687179{col 26}{space 2} .0391281{col 37}{space 5} 1.610425{col 51}{space 3} 1.763934
{txt}{space 8}Peru {c |}{col 14}{res}{space 2} 1.443709{col 26}{space 2} .0599011{col 37}{space 5} 1.326205{col 51}{space 3} 1.561212
{txt}{space 7}Spain {c |}{col 14}{res}{space 2} 1.674033{col 26}{space 2}  .039084{col 37}{space 5} 1.597365{col 51}{space 3} 1.750701
{txt}{space 3}_subpop_6 {c |}{col 14}{res}{space 2}    1.528{col 26}{space 2} .0628115{col 37}{space 5} 1.404787{col 51}{space 3} 1.651213
{txt}{space 3}_subpop_7 {c |}{col 14}{res}{space 2} 1.806283{col 26}{space 2} .0305333{col 37}{space 5} 1.746388{col 51}{space 3} 1.866177
{txt}{space 10}UK {c |}{col 14}{res}{space 2}    1.715{col 26}{space 2} .0357107{col 37}{space 5} 1.644949{col 51}{space 3} 1.785051
{txt}{space 10}US {c |}{col 14}{res}{space 2} 1.751412{col 26}{space 2} .0404862{col 37}{space 5} 1.671994{col 51}{space 3} 1.830831
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. *********************************
. ****CODE FOR OTHER TREATMENT*****
. *****INFORMATION MEASURES********
. *****SKIP AHEAD FOR TESTS *******
. ******RELATED TO FIGURE 1********
. *********************************
. 
. *The below makes Information Acquisition measures out of the 2 quesitons asked after each treamtent, for the remaining treatment conditions in the study: the alternate reminder condition included in the U.S. only, Domestic Terror Threat condition, Economic Threat condition, and Crime Threat condition
. 
. *****INTL TT - Alt Reminder US (questions = mct1c & mct2c)*****
. 
. * Note: this is the alt reminder condition included only U.S.
. 
. *Coding information for mct1c(TT-OR): 1 = more than half (correct answer); 2 = less than half (incorrect answer); 888/988/999 as above
. 
. gen TTORInfo1=mct1c
{txt}(7,555 missing values generated)

{com}. recode TTORInfo1 1=1 2=0 888=0 988=0 999=0
{txt}(TTORInfo1: 22 changes made)

{com}. recode TTORInfo1 .=0 if tt_rdc==1
{txt}(TTORInfo1: 0 changes made)

{com}. 
. *Coding information for mct2c(TT-OR): 1 = less than 100 (incorrect answer); 2 = more than 100 (correct answer); 888/998/999 same as above
. 
. gen TTORInfo2=mct2c
{txt}(7,555 missing values generated)

{com}. recode TTORInfo2 1=0 2=1 888=0 988=0 999=0
{txt}(TTORInfo2: 191 changes made)

{com}. recode TTORInfo2 .=0 if tt_rdc==1
{txt}(TTORInfo2: 0 changes made)

{com}. 
. gen TTORInfo=TTORInfo1+TTORInfo2
{txt}(7,555 missing values generated)

{com}. 
. ***** DOMESTIC TT (questions = mcdt1 & mcdt2)*****
. 
. *DOMESTIC TERROR THREAT
. 
. *Coding information for mcdt1(Domestic Terror Threat Info Q1): 1 = more than half (correct answer); 2 = less than half (incorrect answer); 888/988/999=DK, DK/NR, NR
. 
. gen DTInfo1=mcdt1
{txt}(7,194 missing values generated)

{com}. recode DTInfo1 1=1 2=0 888=0 988=0 999=0
{txt}(DTInfo1: 170 changes made)

{com}. recode DTInfo1 .=0 if dom_tt==1
{txt}(DTInfo1: 7 changes made)

{com}. 
. *Coding information for mcdt2 (Domestic Terror Threat Info Q2): 1 = city 1; 2 = city 2; 888/988/999 as above
. *Putumayo (2) = correct for Ecuador (country==2)
. *Aucayacu (1) = correct for Peru (country==4)
. *Burgos (1)  = correct for Spain (coiuntry==5)
. *Hakkari (1) = correct for Turkey (country==6)
. 
. gen DTInfo2=mcdt2
{txt}(7,194 missing values generated)

{com}. recode DTInfo2 888=0 988=0 999=0
{txt}(DTInfo2: 52 changes made)

{com}. recode DTInfo2 2=1 1=0 if country==2
{txt}(DTInfo2: 87 changes made)

{com}. recode DTInfo2 1=1 2=0 if country==4
{txt}(DTInfo2: 3 changes made)

{com}. recode DTInfo2 1=1 2=0 if country==5
{txt}(DTInfo2: 33 changes made)

{com}. recode DTInfo2 1=1 2=0 if country==6
{txt}(DTInfo2: 24 changes made)

{com}. 
. *generate combined DT variable
. 
. gen DTInfo=DTInfo1+DTInfo2
{txt}(7,194 missing values generated)

{com}. recode DTInfo .=0 if dom_tt==1
{txt}(DTInfo: 7 changes made)

{com}. 
. *****ECO THREAT (questions = mce1 & mce2)*****
. 
. *Coding information for mce1 (Eco Threat Info Q1): 1 = more than half (correct answer); 2 = less than half (incorrect answer); there are no 888/988/999 responses
. 
. gen ETInfo1=mce1 
{txt}(6,687 missing values generated)

{com}. recode ETInfo1 1=1 2=0
{txt}(ETInfo1: 276 changes made)

{com}. recode ETInfo1 .=0 if econ==1
{txt}(ETInfo1: 10 changes made)

{com}. 
. ***recode Spain to reverse it, b/c question was asked so that "more than half" was incorrect (asked about satisfaction, rather than dis...)
. recode ETInfo1 1=0 0=1 if country==5
{txt}(ETInfo1: 188 changes made)

{com}. 
. *Coding information for mce2 (Eco Threat Info Q2): 1 = since WWI (incorrect answer); 2 = since WWII (correct answer); there are no 888/988/999 responses
. 
. gen ETInfo2=mce2
{txt}(6,688 missing values generated)

{com}. recode ETInfo2 1=0 2=1
{txt}(ETInfo2: 1058 changes made)

{com}. recode ETInfo2 .=0 if econ==1
{txt}(ETInfo2: 11 changes made)

{com}. 
. *generate combined ET variable
. 
. gen ETInfo=ETInfo1+ETInfo2
{txt}(6,677 missing values generated)

{com}. 
. ****CRIME THREAT (questions = mcc1 & mcc2)*****
. 
. *Coding information for mcc1 (Crime Threat Info Q1): 1 = more than half (correct answer); 2 = less than half (incorrect answer); there are no 888/988/999 responses
. 
. gen CTInfo1=mcc1 
{txt}(7,654 missing values generated)

{com}. recode CTInfo1 1=1 2=0 888=0 988=0 999=0
{txt}(CTInfo1: 20 changes made)

{com}. recode CTInfo1 .=0 if crime==1
{txt}(CTInfo1: 9 changes made)

{com}. 
. *Coding information for mcc2 (Crime Threat Info Q2): 1 = increased (incorrect answer); 2 = decreased (correct answer); there are no 888/988/999 responses
. 
. gen CTInfo2=mcc2
{txt}(7,654 missing values generated)

{com}. recode CTInfo2 1=1 2=0 888=0 988=0 999=0
{txt}(CTInfo2: 13 changes made)

{com}. recode CTInfo2 .=0 if crime==1
{txt}(CTInfo2: 9 changes made)

{com}. 
. *generate combined CT variable
. 
. gen CTInfo=CTInfo1+CTInfo2
{txt}(7,645 missing values generated)

{com}. 
. 
. *********************************
. ****CODE FOR CHI-SQUARED TESTS***
. *****RELATED TO OUTPUT IN********
. ********** FIGURE 1 *************
. *********************************
. 
. *First, create a single measure for all InfoBoth Question 
.           
. gen InfoBoth=.
{txt}(7,746 missing values generated)

{com}. recode InfoBoth .=0 if GTInfo==0
{txt}(InfoBoth: 357 changes made)

{com}. recode InfoBoth .=1 if GTInfo==1
{txt}(InfoBoth: 334 changes made)

{com}. recode InfoBoth .=2 if GTInfo==2
{txt}(InfoBoth: 739 changes made)

{com}. recode InfoBoth .=0 if TTNRInfo==0
{txt}(InfoBoth: 103 changes made)

{com}. recode InfoBoth .=1 if TTNRInfo==1
{txt}(InfoBoth: 343 changes made)

{com}. recode InfoBoth .=2 if TTNRInfo==2
{txt}(InfoBoth: 1033 changes made)

{com}. recode InfoBoth .=0 if TTRInfo==0
{txt}(InfoBoth: 96 changes made)

{com}. recode InfoBoth .=1 if TTRInfo==1
{txt}(InfoBoth: 343 changes made)

{com}. recode InfoBoth .=2 if TTRInfo==2
{txt}(InfoBoth: 994 changes made)

{com}. recode InfoBoth .=0 if TTORInfo==0
{txt}(InfoBoth: 3 changes made)

{com}. recode InfoBoth .=1 if TTORInfo==1
{txt}(InfoBoth: 29 changes made)

{com}. recode InfoBoth .=2 if TTORInfo==2
{txt}(InfoBoth: 159 changes made)

{com}. recode InfoBoth .=0 if ETInfo==0
{txt}(InfoBoth: 40 changes made)

{com}. recode InfoBoth .=1 if ETInfo==1
{txt}(InfoBoth: 236 changes made)

{com}. recode InfoBoth .=2 if ETInfo==2
{txt}(InfoBoth: 793 changes made)

{com}. recode InfoBoth .=0 if DTInfo==0
{txt}(InfoBoth: 57 changes made)

{com}. recode InfoBoth .=1 if DTInfo==1
{txt}(InfoBoth: 190 changes made)

{com}. recode InfoBoth .=2 if DTInfo==2
{txt}(InfoBoth: 312 changes made)

{com}. recode InfoBoth .=0 if CTInfo==0
{txt}(InfoBoth: 16 changes made)

{com}. recode InfoBoth .=1 if CTInfo==1
{txt}(InfoBoth: 19 changes made)

{com}. recode InfoBoth .=2 if CTInfo==2
{txt}(InfoBoth: 66 changes made)

{com}. 
. *Second generate a series of dummy variables to combine exp conditions (where Good Times=1, terror threat=0)
. gen GT_1vsTTNR_0=.
{txt}(7,746 missing values generated)

{com}. recode GT_1vsTTNR_0 .=1 if gt==1 
{txt}(GT_1vsTTNR_0: 1430 changes made)

{com}. recode GT_1vsTTNR_0 .=0 if tt==1
{txt}(GT_1vsTTNR_0: 1479 changes made)

{com}. 
. gen GT_1vsTTR_0=.
{txt}(7,746 missing values generated)

{com}. recode GT_1vsTTR_0 .=1 if gt==1 
{txt}(GT_1vsTTR_0: 1430 changes made)

{com}. recode GT_1vsTTR_0 .=0 if tt_rbi==1
{txt}(GT_1vsTTR_0: 1433 changes made)

{com}. 
. *Third, run the chi-squared tests reported on in the main text
. sort country
{txt}
{com}. by country: tabulate InfoBoth GT_1vsTTNR_0, chi2

{txt}{hline}
-> country = Albania

           {c |}     GT_1vsTTNR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        13         52 {txt}{c |}{res}        65 
{txt}         1 {c |}{res}        36         20 {txt}{c |}{res}        56 
{txt}         2 {c |}{res}        74         49 {txt}{c |}{res}       123 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       123        121 {txt}{c |}{res}       244 

{txt}          Pearson chi2({res}2{txt}) = {res} 33.0386  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Ecuador

           {c |}     GT_1vsTTNR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        16         43 {txt}{c |}{res}        59 
{txt}         1 {c |}{res}        31         33 {txt}{c |}{res}        64 
{txt}         2 {c |}{res}        54         25 {txt}{c |}{res}        79 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       101        101 {txt}{c |}{res}       202 

{txt}          Pearson chi2({res}2{txt}) = {res} 23.0640  {txt} Pr = {res}0.000

{txt}{hline}
-> country = France

           {c |}     GT_1vsTTNR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         3         40 {txt}{c |}{res}        43 
{txt}         1 {c |}{res}        41         41 {txt}{c |}{res}        82 
{txt}         2 {c |}{res}       140        102 {txt}{c |}{res}       242 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       184        183 {txt}{c |}{res}       367 

{txt}          Pearson chi2({res}2{txt}) = {res} 37.8017  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Peru

           {c |}     GT_1vsTTNR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        22         67 {txt}{c |}{res}        89 
{txt}         1 {c |}{res}        37         47 {txt}{c |}{res}        84 
{txt}         2 {c |}{res}        91         39 {txt}{c |}{res}       130 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       150        153 {txt}{c |}{res}       303 

{txt}          Pearson chi2({res}2{txt}) = {res} 44.7180  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Spain

           {c |}     GT_1vsTTNR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         5         44 {txt}{c |}{res}        49 
{txt}         1 {c |}{res}        47         44 {txt}{c |}{res}        91 
{txt}         2 {c |}{res}       141        106 {txt}{c |}{res}       247 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       193        194 {txt}{c |}{res}       387 

{txt}          Pearson chi2({res}2{txt}) = {res} 36.0969  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Turkey FtoF

           {c |}     GT_1vsTTNR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        24         34 {txt}{c |}{res}        58 
{txt}         1 {c |}{res}        32         32 {txt}{c |}{res}        64 
{txt}         2 {c |}{res}        68         51 {txt}{c |}{res}       119 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       124        117 {txt}{c |}{res}       241 

{txt}          Pearson chi2({res}2{txt}) = {res}  3.9527  {txt} Pr = {res}0.139

{txt}{hline}
-> country = Turkey online

           {c |}     GT_1vsTTNR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         4         24 {txt}{c |}{res}        28 
{txt}         1 {c |}{res}        38         43 {txt}{c |}{res}        81 
{txt}         2 {c |}{res}       161        118 {txt}{c |}{res}       279 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       203        185 {txt}{c |}{res}       388 

{txt}          Pearson chi2({res}2{txt}) = {res} 20.4305  {txt} Pr = {res}0.000

{txt}{hline}
-> country = UK

           {c |}     GT_1vsTTNR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        11         33 {txt}{c |}{res}        44 
{txt}         1 {c |}{res}        49         47 {txt}{c |}{res}        96 
{txt}         2 {c |}{res}       140        110 {txt}{c |}{res}       250 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       200        190 {txt}{c |}{res}       390 

{txt}          Pearson chi2({res}2{txt}) = {res} 14.3947  {txt} Pr = {res}0.001

{txt}{hline}
-> country = US

           {c |}     GT_1vsTTNR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         5         20 {txt}{c |}{res}        25 
{txt}         1 {c |}{res}        32         27 {txt}{c |}{res}        59 
{txt}         2 {c |}{res}       164        139 {txt}{c |}{res}       303 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       201        186 {txt}{c |}{res}       387 

{txt}          Pearson chi2({res}2{txt}) = {res} 10.9214  {txt} Pr = {res}0.004

{txt}
{com}. by country: tabulate InfoBoth GT_1vsTTR_0, chi2

{txt}{hline}
-> country = Albania

           {c |}      GT_1vsTTR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        16         52 {txt}{c |}{res}        68 
{txt}         1 {c |}{res}        43         20 {txt}{c |}{res}        63 
{txt}         2 {c |}{res}        57         49 {txt}{c |}{res}       106 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       116        121 {txt}{c |}{res}       237 

{txt}          Pearson chi2({res}2{txt}) = {res} 27.9664  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Ecuador

           {c |}      GT_1vsTTR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        14         43 {txt}{c |}{res}        57 
{txt}         1 {c |}{res}        31         33 {txt}{c |}{res}        64 
{txt}         2 {c |}{res}        52         25 {txt}{c |}{res}        77 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}        97        101 {txt}{c |}{res}       198 

{txt}          Pearson chi2({res}2{txt}) = {res} 24.2135  {txt} Pr = {res}0.000

{txt}{hline}
-> country = France

           {c |}      GT_1vsTTR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         8         40 {txt}{c |}{res}        48 
{txt}         1 {c |}{res}        45         41 {txt}{c |}{res}        86 
{txt}         2 {c |}{res}       142        102 {txt}{c |}{res}       244 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       195        183 {txt}{c |}{res}       378 

{txt}          Pearson chi2({res}2{txt}) = {res} 27.7237  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Peru

           {c |}      GT_1vsTTR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        22         67 {txt}{c |}{res}        89 
{txt}         1 {c |}{res}        40         47 {txt}{c |}{res}        87 
{txt}         2 {c |}{res}        89         39 {txt}{c |}{res}       128 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       151        153 {txt}{c |}{res}       304 

{txt}          Pearson chi2({res}2{txt}) = {res} 42.8360  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Spain

           {c |}      GT_1vsTTR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         5         44 {txt}{c |}{res}        49 
{txt}         1 {c |}{res}        49         44 {txt}{c |}{res}        93 
{txt}         2 {c |}{res}       127        106 {txt}{c |}{res}       233 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       181        194 {txt}{c |}{res}       375 

{txt}          Pearson chi2({res}2{txt}) = {res} 32.7911  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Turkey FtoF

           {c |}      GT_1vsTTR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        15         34 {txt}{c |}{res}        49 
{txt}         1 {c |}{res}        29         32 {txt}{c |}{res}        61 
{txt}         2 {c |}{res}        81         51 {txt}{c |}{res}       132 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       125        117 {txt}{c |}{res}       242 

{txt}          Pearson chi2({res}2{txt}) = {res} 14.0840  {txt} Pr = {res}0.001

{txt}{hline}
-> country = Turkey online

           {c |}      GT_1vsTTR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         2         24 {txt}{c |}{res}        26 
{txt}         1 {c |}{res}        33         43 {txt}{c |}{res}        76 
{txt}         2 {c |}{res}       156        118 {txt}{c |}{res}       274 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       191        185 {txt}{c |}{res}       376 

{txt}          Pearson chi2({res}2{txt}) = {res} 25.1119  {txt} Pr = {res}0.000

{txt}{hline}
-> country = UK

           {c |}      GT_1vsTTR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         5         33 {txt}{c |}{res}        38 
{txt}         1 {c |}{res}        47         47 {txt}{c |}{res}        94 
{txt}         2 {c |}{res}       148        110 {txt}{c |}{res}       258 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       200        190 {txt}{c |}{res}       390 

{txt}          Pearson chi2({res}2{txt}) = {res} 25.9892  {txt} Pr = {res}0.000

{txt}{hline}
-> country = US

           {c |}      GT_1vsTTR_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         9         20 {txt}{c |}{res}        29 
{txt}         1 {c |}{res}        26         27 {txt}{c |}{res}        53 
{txt}         2 {c |}{res}       142        139 {txt}{c |}{res}       281 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       177        186 {txt}{c |}{res}       363 

{txt}          Pearson chi2({res}2{txt}) = {res}  4.0026  {txt} Pr = {res}0.135

{txt}
{com}. 
. *********************************
. ****CODE FOR TESTS ON FIRST******
. *****QUESTION ONLY; SEE TEXT &***
. ****** APPENDIX FIGURE 1 ********
. *********************************
. 
. *create a single information question for just the first question
. gen Info1=.
{txt}(7,746 missing values generated)

{com}. recode Info1 .=0 if GTInfo1==0
{txt}(Info1: 500 changes made)

{com}. recode Info1 .=1 if GTInfo1==1
{txt}(Info1: 930 changes made)

{com}. recode Info1 .=0 if TTNRInfo1==0
{txt}(Info1: 353 changes made)

{com}. recode Info1 .=1 if TTNRInfo1==1
{txt}(Info1: 1126 changes made)

{com}. recode Info1 .=0 if TTRInfo1==0
{txt}(Info1: 357 changes made)

{com}. recode Info1 .=1 if TTRInfo1==1
{txt}(Info1: 1076 changes made)

{com}. recode Info1 .=0 if TTORInfo1==0
{txt}(Info1: 22 changes made)

{com}. recode Info1 .=1 if TTORInfo1==1
{txt}(Info1: 169 changes made)

{com}. recode Info1 .=0 if ETInfo1==0
{txt}(Info1: 178 changes made)

{com}. recode Info1 .=1 if ETInfo1==1
{txt}(Info1: 891 changes made)

{com}. recode Info1 .=0 if DTInfo1==0
{txt}(Info1: 177 changes made)

{com}. recode Info1 .=1 if DTInfo1==1
{txt}(Info1: 382 changes made)

{com}. recode Info1 .=0 if CTInfo1==0
{txt}(Info1: 29 changes made)

{com}. recode Info1 .=1 if CTInfo1==1
{txt}(Info1: 72 changes made)

{com}. 
. *output for Appendix Figure 1
. sort country
{txt}
{com}. by country: sum GTInfo1

{txt}{hline}
-> country = Albania

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}GTInfo1 {c |}{res}        121    .4958678    .5020619          0          1

{txt}{hline}
-> country = Ecuador

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}GTInfo1 {c |}{res}        101    .4950495    .5024692          0          1

{txt}{hline}
-> country = France

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}GTInfo1 {c |}{res}        183    .6502732    .4781919          0          1

{txt}{hline}
-> country = Peru

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}GTInfo1 {c |}{res}        153    .4313725    .4968944          0          1

{txt}{hline}
-> country = Spain

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}GTInfo1 {c |}{res}        194    .6752577    .4694901          0          1

{txt}{hline}
-> country = Turkey FtoF

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}GTInfo1 {c |}{res}        117    .6239316     .486481          0          1

{txt}{hline}
-> country = Turkey online

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}GTInfo1 {c |}{res}        185    .7837838    .4127805          0          1

{txt}{hline}
-> country = UK

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}GTInfo1 {c |}{res}        190    .6894737    .4639316          0          1

{txt}{hline}
-> country = US

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 5}GTInfo1 {c |}{res}        186    .8333333    .3736839          0          1

{txt}
{com}. by country: sum TTNRInfo1

{txt}{hline}
-> country = Albania

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}TTNRInfo1 {c |}{res}        123    .6666667    .4733326          0          1

{txt}{hline}
-> country = Ecuador

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}TTNRInfo1 {c |}{res}        101    .6138614    .4892913          0          1

{txt}{hline}
-> country = France

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}TTNRInfo1 {c |}{res}        184    .8043478    .3977843          0          1

{txt}{hline}
-> country = Peru

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}TTNRInfo1 {c |}{res}        150    .6733333    .4705654          0          1

{txt}{hline}
-> country = Spain

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}TTNRInfo1 {c |}{res}        193    .7772021     .417206          0          1

{txt}{hline}
-> country = Turkey FtoF

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}TTNRInfo1 {c |}{res}        124    .6935484    .4628898          0          1

{txt}{hline}
-> country = Turkey online

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}TTNRInfo1 {c |}{res}        203    .8522167    .3557623          0          1

{txt}{hline}
-> country = UK

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}TTNRInfo1 {c |}{res}        200         .75    .4340993          0          1

{txt}{hline}
-> country = US

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}TTNRInfo1 {c |}{res}        201    .8656716    .3418562          0          1

{txt}
{com}. by country: sum TTRInfo1

{txt}{hline}
-> country = Albania

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}TTRInfo1 {c |}{res}        116    .5431034    .5002998          0          1

{txt}{hline}
-> country = Ecuador

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}TTRInfo1 {c |}{res}         97    .6185567    .4882643          0          1

{txt}{hline}
-> country = France

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}TTRInfo1 {c |}{res}        195    .7846154     .412147          0          1

{txt}{hline}
-> country = Peru

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}TTRInfo1 {c |}{res}        151    .6556291    .4767439          0          1

{txt}{hline}
-> country = Spain

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}TTRInfo1 {c |}{res}        181    .7458564    .4365865          0          1

{txt}{hline}
-> country = Turkey FtoF

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}TTRInfo1 {c |}{res}        125        .744     .438178          0          1

{txt}{hline}
-> country = Turkey online

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}TTRInfo1 {c |}{res}        191    .8534031    .3546332          0          1

{txt}{hline}
-> country = UK

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}TTRInfo1 {c |}{res}        200         .79    .4083303          0          1

{txt}{hline}
-> country = US

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}TTRInfo1 {c |}{res}        177    .8587571      .34926          0          1

{txt}
{com}. 
. *prtests for Info1 variable, int'l terror threat vs. good times
. by country: prtest Info1, by(GT_1vsTTNR_0)

{txt}{hline}
-> country = Albania

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     123
                                                   1{txt}: Number of obs = {res}     121
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .6666667{col 28} .0425051{col 58} .5833582{col 70} .7499752
           {txt}1 {c |}{res}{col 17} .4958678{col 28}  .045453{col 58} .4067815{col 70}  .584954
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .1707989{col 28} .0622307{col 58}  .048829{col 70} .2927688
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0631545{col 38}    2.70{col 49}0.007
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  2.7045
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.9966         {txt}Pr(|Z| > |z|) = {res}0.0068          {txt}Pr(Z > z) = {res}0.0034

{txt}{hline}
-> country = Ecuador

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     101
                                                   1{txt}: Number of obs = {res}     101
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .6138614{col 28} .0484447{col 58} .5189116{col 70} .7088112
           {txt}1 {c |}{res}{col 17} .4950495{col 28} .0497494{col 58} .3975424{col 70} .5925566
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .1188119{col 28} .0694398{col 58}-.0172877{col 70} .2549115
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0699412{col 38}    1.70{col 49}0.089
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  1.6987
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.9553         {txt}Pr(|Z| > |z|) = {res}0.0894          {txt}Pr(Z > z) = {res}0.0447

{txt}{hline}
-> country = France

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     184
                                                   1{txt}: Number of obs = {res}     183
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .8043478{col 28} .0292453{col 58} .7470282{col 70} .8616675
           {txt}1 {c |}{res}{col 17} .6502732{col 28} .0352522{col 58} .5811801{col 70} .7193664
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .1540746{col 28}  .045804{col 58} .0643004{col 70} .2438488
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0464824{col 38}    3.31{col 49}0.001
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  3.3147
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.9995         {txt}Pr(|Z| > |z|) = {res}0.0009          {txt}Pr(Z > z) = {res}0.0005

{txt}{hline}
-> country = Peru

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     150
                                                   1{txt}: Number of obs = {res}     153
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .6733333{col 28} .0382932{col 58}   .59828{col 70} .7483867
           {txt}1 {c |}{res}{col 17} .4313725{col 28}   .04004{col 58} .3528955{col 70} .5098496
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .2419608{col 28} .0554037{col 58} .1333714{col 70} .3505501
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0571498{col 38}    4.23{col 49}0.000
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  4.2338
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0000          {txt}Pr(Z > z) = {res}0.0000

{txt}{hline}
-> country = Spain

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     193
                                                   1{txt}: Number of obs = {res}     194
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .7772021{col 28} .0299532{col 58} .7184948{col 70} .8359094
           {txt}1 {c |}{res}{col 17} .6752577{col 28} .0336204{col 58} .6093629{col 70} .7411526
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .1019443{col 28} .0450281{col 58} .0136909{col 70} .1901978
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0453389{col 38}    2.25{col 49}0.025
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  2.2485
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.9877         {txt}Pr(|Z| > |z|) = {res}0.0245          {txt}Pr(Z > z) = {res}0.0123

{txt}{hline}
-> country = Turkey FtoF

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     124
                                                   1{txt}: Number of obs = {res}     117
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .6935484{col 28} .0414008{col 58} .6124044{col 70} .7746924
           {txt}1 {c |}{res}{col 17} .6239316{col 28} .0447826{col 58} .5361594{col 70} .7117038
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .0696168{col 28} .0609877{col 58} -.049917{col 70} .1891505
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0610651{col 38}    1.14{col 49}0.254
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  1.1400
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.8729         {txt}Pr(|Z| > |z|) = {res}0.2543          {txt}Pr(Z > z) = {res}0.1271

{txt}{hline}
-> country = Turkey online

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     203
                                                   1{txt}: Number of obs = {res}     185
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .8522167{col 28}  .024908{col 58} .8033979{col 70} .9010356
           {txt}1 {c |}{res}{col 17} .7837838{col 28} .0302661{col 58} .7244633{col 70} .8431042
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17}  .068433{col 28} .0391975{col 58}-.0083928{col 70} .1452587
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0390852{col 38}    1.75{col 49}0.080
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  1.7509
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.9600         {txt}Pr(|Z| > |z|) = {res}0.0800          {txt}Pr(Z > z) = {res}0.0400

{txt}{hline}
-> country = UK

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     200
                                                   1{txt}: Number of obs = {res}     190
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17}      .75{col 28} .0306186{col 58} .6899886{col 70} .8100114
           {txt}1 {c |}{res}{col 17} .6894737{col 28} .0335684{col 58} .6236807{col 70} .7552666
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .0605263{col 28}  .045435{col 58}-.0285247{col 70} .1495773
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0454613{col 38}    1.33{col 49}0.183
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  1.3314
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.9085         {txt}Pr(|Z| > |z|) = {res}0.1831          {txt}Pr(Z > z) = {res}0.0915

{txt}{hline}
-> country = US

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     201
                                                   1{txt}: Number of obs = {res}     186
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .8656716{col 28} .0240526{col 58} .8185294{col 70} .9128139
           {txt}1 {c |}{res}{col 17} .8333333{col 28} .0273261{col 58} .7797752{col 70} .8868915
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .0323383{col 28} .0364039{col 58} -.039012{col 70} .1036886
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0363163{col 38}    0.89{col 49}0.373
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  0.8905
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.8134         {txt}Pr(|Z| > |z|) = {res}0.3732          {txt}Pr(Z > z) = {res}0.1866
{txt}
{com}. by country: prtest Info1, by(GT_1vsTTR_0)

{txt}{hline}
-> country = Albania

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     116
                                                   1{txt}: Number of obs = {res}     121
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .5431034{col 28}  .046251{col 58} .4524531{col 70} .6337538
           {txt}1 {c |}{res}{col 17} .4958678{col 28}  .045453{col 58} .4067815{col 70}  .584954
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .0472357{col 28}  .064847{col 58}-.0798621{col 70} .1743334
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0649246{col 38}    0.73{col 49}0.467
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  0.7275
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.7666         {txt}Pr(|Z| > |z|) = {res}0.4669          {txt}Pr(Z > z) = {res}0.2334

{txt}{hline}
-> country = Ecuador

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}      97
                                                   1{txt}: Number of obs = {res}     101
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .6185567{col 28} .0493195{col 58} .5218922{col 70} .7152212
           {txt}1 {c |}{res}{col 17} .4950495{col 28} .0497494{col 58} .3975424{col 70} .5925566
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .1235072{col 28}  .070053{col 58}-.0137941{col 70} .2608085
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0706413{col 38}    1.75{col 49}0.080
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  1.7484
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.9598         {txt}Pr(|Z| > |z|) = {res}0.0804          {txt}Pr(Z > z) = {res}0.0402

{txt}{hline}
-> country = France

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     195
                                                   1{txt}: Number of obs = {res}     183
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .7846154{col 28} .0294387{col 58} .7269166{col 70} .8423142
           {txt}1 {c |}{res}{col 17} .6502732{col 28} .0352522{col 58} .5811801{col 70} .7193664
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .1343422{col 28} .0459277{col 58} .0443254{col 70} .2243589
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0462326{col 38}    2.91{col 49}0.004
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  2.9058
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.9982         {txt}Pr(|Z| > |z|) = {res}0.0037          {txt}Pr(Z > z) = {res}0.0018

{txt}{hline}
-> country = Peru

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     151
                                                   1{txt}: Number of obs = {res}     153
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .6556291{col 28} .0386682{col 58} .5798409{col 70} .7314174
           {txt}1 {c |}{res}{col 17} .4313725{col 28}   .04004{col 58} .3528955{col 70} .5098496
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .2242566{col 28} .0556636{col 58}  .115158{col 70} .3333552
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28}  .057145{col 38}    3.92{col 49}0.000
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  3.9243
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}1.0000         {txt}Pr(|Z| > |z|) = {res}0.0001          {txt}Pr(Z > z) = {res}0.0000

{txt}{hline}
-> country = Spain

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     181
                                                   1{txt}: Number of obs = {res}     194
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .7458564{col 28} .0323614{col 58} .6824291{col 70} .8092836
           {txt}1 {c |}{res}{col 17} .6752577{col 28} .0336204{col 58} .6093629{col 70} .7411526
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .0705986{col 28} .0466647{col 58}-.0208626{col 70} .1620598
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0469244{col 38}    1.50{col 49}0.132
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  1.5045
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.9338         {txt}Pr(|Z| > |z|) = {res}0.1324          {txt}Pr(Z > z) = {res}0.0662

{txt}{hline}
-> country = Turkey FtoF

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     125
                                                   1{txt}: Number of obs = {res}     117
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17}     .744{col 28} .0390348{col 58} .6674933{col 70} .8205067
           {txt}1 {c |}{res}{col 17} .6239316{col 28} .0447826{col 58} .5361594{col 70} .7117038
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .1200684{col 28}  .059407{col 58} .0036328{col 70} .2365039
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0597042{col 38}    2.01{col 49}0.044
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  2.0111
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.9778         {txt}Pr(|Z| > |z|) = {res}0.0443          {txt}Pr(Z > z) = {res}0.0222

{txt}{hline}
-> country = Turkey online

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     191
                                                   1{txt}: Number of obs = {res}     185
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .8534031{col 28} .0255931{col 58} .8032416{col 70} .9035647
           {txt}1 {c |}{res}{col 17} .7837838{col 28} .0302661{col 58} .7244633{col 70} .8431042
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .0696194{col 28} .0396364{col 58}-.0080665{col 70} .1473052
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0397039{col 38}    1.75{col 49}0.080
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  1.7535
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.9602         {txt}Pr(|Z| > |z|) = {res}0.0795          {txt}Pr(Z > z) = {res}0.0398

{txt}{hline}
-> country = UK

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     200
                                                   1{txt}: Number of obs = {res}     190
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17}      .79{col 28}  .028801{col 58}  .733551{col 70}  .846449
           {txt}1 {c |}{res}{col 17} .6894737{col 28} .0335684{col 58} .6236807{col 70} .7552666
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .1005263{col 28} .0442305{col 58} .0138361{col 70} .1872166
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0443798{col 38}    2.27{col 49}0.024
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  2.2651
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.9882         {txt}Pr(|Z| > |z|) = {res}0.0235          {txt}Pr(Z > z) = {res}0.0118

{txt}{hline}
-> country = US

Two-sample test of proportions                     {res}0{txt}: Number of obs = {res}     177
                                                   1{txt}: Number of obs = {res}     186
{txt}{hline 13}{c TT}{hline 64}
    Variable {c |}{col 22}Mean{col 29}Std. Err.{col 44}z{col 49}P>|z|{col 59}[95% Conf. Interval]
{hline 13}{c +}{hline 64}
{col 12}0{col 14}{c |}{res}{col 17} .8587571{col 28} .0261777{col 58} .8074497{col 70} .9100645
           {txt}1 {c |}{res}{col 17} .8333333{col 28} .0273261{col 58} .7797752{col 70} .8868915
{txt}{hline 13}{c +}{hline 64}
        diff {c |}{res}{col 17} .0254237{col 28} .0378416{col 58}-.0487445{col 70} .0995919
{txt}{col 14}{c |}{col 17}under Ho:{res}{col 28} .0379286{col 38}    0.67{col 49}0.503
{txt}{hline 13}{c BT}{hline 64}
        diff = prop({res}0{txt}) - prop({res}1{txt})                                  z = {res}  0.6703
{txt}    Ho: diff = 0

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(Z < z) = {res}0.7487         {txt}Pr(|Z| > |z|) = {res}0.5027          {txt}Pr(Z > z) = {res}0.2513
{txt}
{com}. 
. 
. *********************************
. ****OUTPUT FOR APPENDIX FIGURE 2**
. ********DISCUSSED IN TEXT********
. *********************************
. sort country
{txt}
{com}. by country: sum DTInfo

{txt}{hline}
-> country = Albania

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}DTInfo {c |}{res}          0

{txt}{hline}
-> country = Ecuador

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}DTInfo {c |}{res}        100        1.34    .7137891          0          2

{txt}{hline}
-> country = France

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}DTInfo {c |}{res}          0

{txt}{hline}
-> country = Peru

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}DTInfo {c |}{res}        155    1.470968     .667518          0          2

{txt}{hline}
-> country = Spain

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}DTInfo {c |}{res}        192    1.546875    .6206656          0          2

{txt}{hline}
-> country = Turkey FtoF

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}DTInfo {c |}{res}        112    1.383929    .7133923          0          2

{txt}{hline}
-> country = Turkey online

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}DTInfo {c |}{res}          0

{txt}{hline}
-> country = UK

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}DTInfo {c |}{res}          0

{txt}{hline}
-> country = US

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}DTInfo {c |}{res}          0

{txt}
{com}. by country: sum ETInfo

{txt}{hline}
-> country = Albania

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}ETInfo {c |}{res}        117    1.495726      .67752          0          2

{txt}{hline}
-> country = Ecuador

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}ETInfo {c |}{res}          0

{txt}{hline}
-> country = France

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}ETInfo {c |}{res}        188    1.664894    .5368172          0          2

{txt}{hline}
-> country = Peru

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}ETInfo {c |}{res}          0

{txt}{hline}
-> country = Spain

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}ETInfo {c |}{res}        188     1.68617    .5495941          0          2

{txt}{hline}
-> country = Turkey FtoF

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}ETInfo {c |}{res}          0

{txt}{hline}
-> country = Turkey online

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}ETInfo {c |}{res}        176    1.630682     .560583          0          2

{txt}{hline}
-> country = UK

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}ETInfo {c |}{res}        204    1.759804    .4924574          0          2

{txt}{hline}
-> country = US

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}ETInfo {c |}{res}        196    1.892857    .3262058          0          2

{txt}
{com}. by country: sum CTInfo

{txt}{hline}
-> country = Albania

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}CTInfo {c |}{res}          0

{txt}{hline}
-> country = Ecuador

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}CTInfo {c |}{res}        101     1.49505    .7566209          0          2

{txt}{hline}
-> country = France

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}CTInfo {c |}{res}          0

{txt}{hline}
-> country = Peru

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}CTInfo {c |}{res}          0

{txt}{hline}
-> country = Spain

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}CTInfo {c |}{res}          0

{txt}{hline}
-> country = Turkey FtoF

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}CTInfo {c |}{res}          0

{txt}{hline}
-> country = Turkey online

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}CTInfo {c |}{res}          0

{txt}{hline}
-> country = UK

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}CTInfo {c |}{res}          0

{txt}{hline}
-> country = US

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}CTInfo {c |}{res}          0

{txt}
{com}. 
. *generate more dummy variables to combine exp conditions (where Good Times=1, threat=0)
. gen GT_1vsDT_0=.
{txt}(7,746 missing values generated)

{com}. recode GT_1vsDT_0 .=1 if gt==1 
{txt}(GT_1vsDT_0: 1430 changes made)

{com}. recode GT_1vsDT_0 .=0 if dom_tt==1
{txt}(GT_1vsDT_0: 559 changes made)

{com}. 
. gen GT_1vsET_0=.
{txt}(7,746 missing values generated)

{com}. recode GT_1vsET_0 .=1 if gt==1 
{txt}(GT_1vsET_0: 1430 changes made)

{com}. recode GT_1vsET_0 .=0 if econ==1
{txt}(GT_1vsET_0: 1069 changes made)

{com}. 
. gen GT_1vsCT_0=.
{txt}(7,746 missing values generated)

{com}. recode GT_1vsCT_0 .=1 if gt==1 
{txt}(GT_1vsCT_0: 1430 changes made)

{com}. recode GT_1vsCT_0 .=0 if crime==1
{txt}(GT_1vsCT_0: 101 changes made)

{com}. 
. 
. by country: tabulate InfoBoth GT_1vsDT_0, chi2

{txt}{hline}
-> country = Albania

           {c |} GT_1vsDT_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        52 {txt}{c |}{res}        52 
{txt}         1 {c |}{res}        20 {txt}{c |}{res}        20 
{txt}         2 {c |}{res}        49 {txt}{c |}{res}        49 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       121 {txt}{c |}{res}       121 


{txt}{hline}
-> country = Ecuador

           {c |}      GT_1vsDT_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        14         43 {txt}{c |}{res}        57 
{txt}         1 {c |}{res}        38         33 {txt}{c |}{res}        71 
{txt}         2 {c |}{res}        48         25 {txt}{c |}{res}        73 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       100        101 {txt}{c |}{res}       201 

{txt}          Pearson chi2({res}2{txt}) = {res} 22.3487  {txt} Pr = {res}0.000

{txt}{hline}
-> country = France

           {c |} GT_1vsDT_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        40 {txt}{c |}{res}        40 
{txt}         1 {c |}{res}        41 {txt}{c |}{res}        41 
{txt}         2 {c |}{res}       102 {txt}{c |}{res}       102 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       183 {txt}{c |}{res}       183 


{txt}{hline}
-> country = Peru

           {c |}      GT_1vsDT_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        15         67 {txt}{c |}{res}        82 
{txt}         1 {c |}{res}        52         47 {txt}{c |}{res}        99 
{txt}         2 {c |}{res}        88         39 {txt}{c |}{res}       127 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       155        153 {txt}{c |}{res}       308 

{txt}          Pearson chi2({res}2{txt}) = {res} 52.1229  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Spain

           {c |}      GT_1vsDT_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        13         44 {txt}{c |}{res}        57 
{txt}         1 {c |}{res}        61         44 {txt}{c |}{res}       105 
{txt}         2 {c |}{res}       118        106 {txt}{c |}{res}       224 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       192        194 {txt}{c |}{res}       386 

{txt}          Pearson chi2({res}2{txt}) = {res} 20.2451  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Turkey FtoF

           {c |}      GT_1vsDT_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        15         34 {txt}{c |}{res}        49 
{txt}         1 {c |}{res}        39         32 {txt}{c |}{res}        71 
{txt}         2 {c |}{res}        58         51 {txt}{c |}{res}       109 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       112        117 {txt}{c |}{res}       229 

{txt}          Pearson chi2({res}2{txt}) = {res}  8.4019  {txt} Pr = {res}0.015

{txt}{hline}
-> country = Turkey online

           {c |} GT_1vsDT_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        24 {txt}{c |}{res}        24 
{txt}         1 {c |}{res}        43 {txt}{c |}{res}        43 
{txt}         2 {c |}{res}       118 {txt}{c |}{res}       118 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       185 {txt}{c |}{res}       185 


{txt}{hline}
-> country = UK

           {c |} GT_1vsDT_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        33 {txt}{c |}{res}        33 
{txt}         1 {c |}{res}        47 {txt}{c |}{res}        47 
{txt}         2 {c |}{res}       110 {txt}{c |}{res}       110 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       190 {txt}{c |}{res}       190 


{txt}{hline}
-> country = US

           {c |} GT_1vsDT_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        20 {txt}{c |}{res}        20 
{txt}         1 {c |}{res}        27 {txt}{c |}{res}        27 
{txt}         2 {c |}{res}       139 {txt}{c |}{res}       139 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       186 {txt}{c |}{res}       186 


{txt}
{com}. by country: tabulate InfoBoth GT_1vsET_0, chi2

{txt}{hline}
-> country = Albania

           {c |}      GT_1vsET_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        12         52 {txt}{c |}{res}        64 
{txt}         1 {c |}{res}        35         20 {txt}{c |}{res}        55 
{txt}         2 {c |}{res}        70         49 {txt}{c |}{res}       119 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       117        121 {txt}{c |}{res}       238 

{txt}          Pearson chi2({res}2{txt}) = {res} 32.7388  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Ecuador

           {c |} GT_1vsET_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        43 {txt}{c |}{res}        43 
{txt}         1 {c |}{res}        33 {txt}{c |}{res}        33 
{txt}         2 {c |}{res}        25 {txt}{c |}{res}        25 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       101 {txt}{c |}{res}       101 


{txt}{hline}
-> country = France

           {c |}      GT_1vsET_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         6         40 {txt}{c |}{res}        46 
{txt}         1 {c |}{res}        51         41 {txt}{c |}{res}        92 
{txt}         2 {c |}{res}       131        102 {txt}{c |}{res}       233 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       188        183 {txt}{c |}{res}       371 

{txt}          Pearson chi2({res}2{txt}) = {res} 29.7649  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Peru

           {c |} GT_1vsET_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        67 {txt}{c |}{res}        67 
{txt}         1 {c |}{res}        47 {txt}{c |}{res}        47 
{txt}         2 {c |}{res}        39 {txt}{c |}{res}        39 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       153 {txt}{c |}{res}       153 


{txt}{hline}
-> country = Spain

           {c |}      GT_1vsET_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         8         44 {txt}{c |}{res}        52 
{txt}         1 {c |}{res}        43         44 {txt}{c |}{res}        87 
{txt}         2 {c |}{res}       137        106 {txt}{c |}{res}       243 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       188        194 {txt}{c |}{res}       382 

{txt}          Pearson chi2({res}2{txt}) = {res} 28.8022  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Turkey FtoF

           {c |} GT_1vsET_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        34 {txt}{c |}{res}        34 
{txt}         1 {c |}{res}        32 {txt}{c |}{res}        32 
{txt}         2 {c |}{res}        51 {txt}{c |}{res}        51 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       117 {txt}{c |}{res}       117 


{txt}{hline}
-> country = Turkey online

           {c |}      GT_1vsET_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         7         24 {txt}{c |}{res}        31 
{txt}         1 {c |}{res}        51         43 {txt}{c |}{res}        94 
{txt}         2 {c |}{res}       118        118 {txt}{c |}{res}       236 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       176        185 {txt}{c |}{res}       361 

{txt}          Pearson chi2({res}2{txt}) = {res}  9.7851  {txt} Pr = {res}0.008

{txt}{hline}
-> country = UK

           {c |}      GT_1vsET_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         6         33 {txt}{c |}{res}        39 
{txt}         1 {c |}{res}        37         47 {txt}{c |}{res}        84 
{txt}         2 {c |}{res}       161        110 {txt}{c |}{res}       271 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       204        190 {txt}{c |}{res}       394 

{txt}          Pearson chi2({res}2{txt}) = {res} 29.0197  {txt} Pr = {res}0.000

{txt}{hline}
-> country = US

           {c |}      GT_1vsET_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}         1         20 {txt}{c |}{res}        21 
{txt}         1 {c |}{res}        19         27 {txt}{c |}{res}        46 
{txt}         2 {c |}{res}       176        139 {txt}{c |}{res}       315 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       196        186 {txt}{c |}{res}       382 

{txt}          Pearson chi2({res}2{txt}) = {res} 22.6816  {txt} Pr = {res}0.000

{txt}
{com}. by country: tabulate InfoBoth GT_1vsCT_0, chi2

{txt}{hline}
-> country = Albania

           {c |} GT_1vsCT_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        52 {txt}{c |}{res}        52 
{txt}         1 {c |}{res}        20 {txt}{c |}{res}        20 
{txt}         2 {c |}{res}        49 {txt}{c |}{res}        49 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       121 {txt}{c |}{res}       121 


{txt}{hline}
-> country = Ecuador

           {c |}      GT_1vsCT_0
  InfoBoth {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        16         43 {txt}{c |}{res}        59 
{txt}         1 {c |}{res}        19         33 {txt}{c |}{res}        52 
{txt}         2 {c |}{res}        66         25 {txt}{c |}{res}        91 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       101        101 {txt}{c |}{res}       202 

{txt}          Pearson chi2({res}2{txt}) = {res} 34.5977  {txt} Pr = {res}0.000

{txt}{hline}
-> country = France

           {c |} GT_1vsCT_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        40 {txt}{c |}{res}        40 
{txt}         1 {c |}{res}        41 {txt}{c |}{res}        41 
{txt}         2 {c |}{res}       102 {txt}{c |}{res}       102 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       183 {txt}{c |}{res}       183 


{txt}{hline}
-> country = Peru

           {c |} GT_1vsCT_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        67 {txt}{c |}{res}        67 
{txt}         1 {c |}{res}        47 {txt}{c |}{res}        47 
{txt}         2 {c |}{res}        39 {txt}{c |}{res}        39 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       153 {txt}{c |}{res}       153 


{txt}{hline}
-> country = Spain

           {c |} GT_1vsCT_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        44 {txt}{c |}{res}        44 
{txt}         1 {c |}{res}        44 {txt}{c |}{res}        44 
{txt}         2 {c |}{res}       106 {txt}{c |}{res}       106 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       194 {txt}{c |}{res}       194 


{txt}{hline}
-> country = Turkey FtoF

           {c |} GT_1vsCT_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        34 {txt}{c |}{res}        34 
{txt}         1 {c |}{res}        32 {txt}{c |}{res}        32 
{txt}         2 {c |}{res}        51 {txt}{c |}{res}        51 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       117 {txt}{c |}{res}       117 


{txt}{hline}
-> country = Turkey online

           {c |} GT_1vsCT_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        24 {txt}{c |}{res}        24 
{txt}         1 {c |}{res}        43 {txt}{c |}{res}        43 
{txt}         2 {c |}{res}       118 {txt}{c |}{res}       118 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       185 {txt}{c |}{res}       185 


{txt}{hline}
-> country = UK

           {c |} GT_1vsCT_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        33 {txt}{c |}{res}        33 
{txt}         1 {c |}{res}        47 {txt}{c |}{res}        47 
{txt}         2 {c |}{res}       110 {txt}{c |}{res}       110 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       190 {txt}{c |}{res}       190 


{txt}{hline}
-> country = US

           {c |} GT_1vsCT_0
  InfoBoth {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}        20 {txt}{c |}{res}        20 
{txt}         1 {c |}{res}        27 {txt}{c |}{res}        27 
{txt}         2 {c |}{res}       139 {txt}{c |}{res}       139 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       186 {txt}{c |}{res}       186 


{txt}
{com}. 
. *********************************
. ****GENERATE RETURN TO ARTICLE***
. **********MEASURES FOR***********
. **********FIGURES 2 & 3**********
. *********************************
.                          
. *Coding information: "back_" denotes whether they went back to the article (online studies only); is coded 1=returned, 0=did not
. 
. *create a combined return variable for the Good Times treatment
. gen gt_return=back_mcn1+back_mcn2
{txt}(6,808 missing values generated)

{com}. 
. *create combined measures of return for each Int'l TT treatment
. gen ttnr_return=back_mct1a+back_mct2a
{txt}(6,765 missing values generated)

{com}. gen ttr_return=back_mct1b+back_mct2b
{txt}(6,805 missing values generated)

{com}. gen ttor_return=back_mct1c+back_mct2c
{txt}(7,555 missing values generated)

{com}. 
. *create a combined measure of return for the econ threat treatment
. gen et_return=back_mce1+back_mce2
{txt}(6,795 missing values generated)

{com}. 
. *create a combined measure of return for the domestic tt treatment
. gen dt_return=back_mcdt1+back_mcdt2
{txt}(7,554 missing values generated)

{com}. 
. *create a "returned to article" = 1 (vs. did not return=0) for all int'l tt conditions combined
. gen back_ttall1=.
{txt}(7,746 missing values generated)

{com}. recode back_ttall1 .=1 if back_mct1a==1
{txt}(back_ttall1: 226 changes made)

{com}. recode back_ttall1 .=1 if back_mct1b==1
{txt}(back_ttall1: 207 changes made)

{com}. recode back_ttall1 .=1 if back_mct1c==1
{txt}(back_ttall1: 57 changes made)

{com}. recode back_ttall1 .=0 if back_mct1a==0
{txt}(back_ttall1: 755 changes made)

{com}. recode back_ttall1 .=0 if back_mct1b==0
{txt}(back_ttall1: 734 changes made)

{com}. recode back_ttall1 .=0 if back_mct1c==0
{txt}(back_ttall1: 134 changes made)

{com}. 
. gen back_ttall2=.
{txt}(7,746 missing values generated)

{com}. recode back_ttall2 .=1 if back_mct2a==1
{txt}(back_ttall2: 135 changes made)

{com}. recode back_ttall2 .=1 if back_mct2b==1
{txt}(back_ttall2: 133 changes made)

{com}. recode back_ttall2 .=1 if back_mct2c==1
{txt}(back_ttall2: 33 changes made)

{com}. recode back_ttall2 .=0 if back_mct2a==0
{txt}(back_ttall2: 846 changes made)

{com}. recode back_ttall2 .=0 if back_mct2b==0
{txt}(back_ttall2: 810 changes made)

{com}. recode back_ttall2 .=0 if back_mct2c==0
{txt}(back_ttall2: 158 changes made)

{com}. 
. *combine the above for a single returned to article measure
. gen ttall_return=back_ttall1+back_ttall2
{txt}(5,633 missing values generated)

{com}. 
. *create a combined return variable for all
. gen return=.
{txt}(7,746 missing values generated)

{com}. recode return .=0 if gt_return==0
{txt}(return: 716 changes made)

{com}. recode return .=1 if gt_return==1
{txt}(return: 200 changes made)

{com}. recode return .=2 if gt_return==2
{txt}(return: 22 changes made)

{com}. recode return .=0 if dt_return==0
{txt}(return: 123 changes made)

{com}. recode return .=1 if dt_return==1
{txt}(return: 63 changes made)

{com}. recode return .=2 if dt_return==2
{txt}(return: 6 changes made)

{com}. recode return .=0 if et_return==0
{txt}(return: 669 changes made)

{com}. recode return .=1 if et_return==1
{txt}(return: 224 changes made)

{com}. recode return .=2 if et_return==2
{txt}(return: 58 changes made)

{com}. recode return .=0 if ttnr_return==0
{txt}(return: 657 changes made)

{com}. recode return .=1 if ttnr_return==1
{txt}(return: 287 changes made)

{com}. recode return .=2 if ttnr_return==2
{txt}(return: 37 changes made)

{com}. recode return .=0 if ttr_return==0
{txt}(return: 640 changes made)

{com}. recode return .=1 if ttr_return==1
{txt}(return: 263 changes made)

{com}. recode return .=2 if ttr_return==2
{txt}(return: 38 changes made)

{com}. 
. *Create info scores for those who did NOT return to the article 
.                   
. *create info scores for GT, only for those who did not return to the article
. gen GTInfo1noreturn=GTInfo1
{txt}(6,316 missing values generated)

{com}. recode GTInfo1noreturn 1=0 if back_mcn1==1
{txt}(GTInfo1noreturn: 176 changes made)

{com}. recode GTInfo1noreturn 0=0 if back_mcn1==1
{txt}(GTInfo1noreturn: 0 changes made)

{com}. gen GTInfo2noreturn=GTInfo2
{txt}(6,316 missing values generated)

{com}. recode GTInfo2noreturn 1=0 if back_mcn2==1
{txt}(GTInfo2noreturn: 50 changes made)

{com}. recode GTInfo2noreturn 0=0 if back_mcn2==1
{txt}(GTInfo2noreturn: 0 changes made)

{com}. 
. gen GTInfonoreturn=GTInfo1noreturn+GTInfo2noreturn
{txt}(6,316 missing values generated)

{com}. 
. *create info scores for ET, only for those who did not return to the article
. 
. gen ETInfo1noreturn=ETInfo1
{txt}(6,677 missing values generated)

{com}. recode ETInfo1noreturn 1=0 if back_mce1==1
{txt}(ETInfo1noreturn: 123 changes made)

{com}. recode ETInfo1noreturn 0=0 if back_mce1==1
{txt}(ETInfo1noreturn: 0 changes made)

{com}. gen ETInfo2noreturn=ETInfo2
{txt}(6,677 missing values generated)

{com}. recode ETInfo2noreturn 1=0 if back_mce2==1
{txt}(ETInfo2noreturn: 192 changes made)

{com}. recode ETInfo2noreturn 0=0 if back_mce2==1
{txt}(ETInfo2noreturn: 0 changes made)

{com}. 
. gen ETInfonoreturn=ETInfo1noreturn+ETInfo2noreturn
{txt}(6,677 missing values generated)

{com}. 
. *create info scores for TT-NR, only for those who did not return to the article
. 
. gen TTNRInfo1noreturn=TTNRInfo1
{txt}(6,267 missing values generated)

{com}. recode TTNRInfo1noreturn 1=0 if back_mct1a==1
{txt}(TTNRInfo1noreturn: 200 changes made)

{com}. recode TTNRInfo1noreturn 0=0 if back_mct1a==1
{txt}(TTNRInfo1noreturn: 0 changes made)

{com}. gen TTNRInfo2noreturn=TTNRInfo2
{txt}(6,267 missing values generated)

{com}. recode TTNRInfo2noreturn 1=0 if back_mct2a==1
{txt}(TTNRInfo2noreturn: 133 changes made)

{com}. recode TTNRInfo2noreturn 0=0 if back_mct2a==1
{txt}(TTNRInfo2noreturn: 0 changes made)

{com}. 
. gen TTNRInfonoreturn=TTNRInfo1noreturn+TTNRInfo2noreturn
{txt}(6,267 missing values generated)

{com}. 
. *create info scores for TT-R, only for those who did not returnto the article
. 
. gen TTRInfo1noreturn=TTRInfo1
{txt}(6,313 missing values generated)

{com}. recode TTRInfo1noreturn 1=0 if back_mct1b==1
{txt}(TTRInfo1noreturn: 187 changes made)

{com}. recode TTRInfo1noreturn 0=0 if back_mct1b==1
{txt}(TTRInfo1noreturn: 0 changes made)

{com}. gen TTRInfo2noreturn=TTRInfo2
{txt}(6,313 missing values generated)

{com}. recode TTRInfo2noreturn 1=0 if back_mct2b==1
{txt}(TTRInfo2noreturn: 130 changes made)

{com}. recode TTRInfo2noreturn 0=0 if back_mct2b==1
{txt}(TTRInfo2noreturn: 0 changes made)

{com}. 
. gen TTRInfonoreturn=TTRInfo1noreturn+TTRInfo2noreturn
{txt}(6,313 missing values generated)

{com}. 
. *create info scores for DT, only for those who did not return to the article
. 
. gen DTInfo1noreturn=DTInfo1
{txt}(7,187 missing values generated)

{com}. recode DTInfo1noreturn 1=0 if back_mcdt1==1
{txt}(DTInfo1noreturn: 26 changes made)

{com}. recode DTInfo1noreturn 0=0 if back_mcdt1==1
{txt}(DTInfo1noreturn: 0 changes made)

{com}. gen DTInfo2noreturn=DTInfo2
{txt}(7,194 missing values generated)

{com}. recode DTInfo2noreturn 1=0 if back_mcdt2==1
{txt}(DTInfo2noreturn: 39 changes made)

{com}. recode DTInfo2noreturn 0=0 if back_mcdt2==1
{txt}(DTInfo2noreturn: 0 changes made)

{com}. 
. gen DTInfonoreturn=DTInfo1noreturn+DTInfo2noreturn
{txt}(7,194 missing values generated)

{com}. 
. *********************************
. *******GENERATE OUTPUT FOR*******
. ************ FIGURE 2 ***********
. *********************************
. 
. *below generates the "mere recall" output for figure 2
. 
. mean GTInfonoreturn, over(country), if online==1
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}       938

       {txt}France: country = {res}France
        {txt}Spain: country = {res}Spain
    {txt}_subpop_3: country = {res}Turkey online
           {txt}UK: country = {res}UK
           {txt}US: country = {res}US

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 1}          Over{col 16}{c |}       Mean{col 28}   Std. Err.{col 40}     [95% Con{col 53}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{res}GTInfonoreturn {txt}{c |}
{space 8}France {c |}{col 16}{res}{space 2}  1.15847{col 28}{space 2} .0616055{col 39}{space 5} 1.037569{col 53}{space 3} 1.279371
{txt}{space 9}Spain {c |}{col 16}{res}{space 2} 1.108247{col 28}{space 2} .0577166{col 39}{space 5} .9949787{col 53}{space 3} 1.221516
{txt}{space 5}_subpop_3 {c |}{col 16}{res}{space 2} 1.210811{col 28}{space 2}  .057047{col 39}{space 5} 1.098856{col 53}{space 3} 1.322765
{txt}{space 12}UK {c |}{col 16}{res}{space 2} 1.121053{col 28}{space 2} .0563969{col 39}{space 5} 1.010374{col 53}{space 3} 1.231732
{txt}{space 12}US {c |}{col 16}{res}{space 2} 1.408602{col 28}{space 2} .0519725{col 39}{space 5} 1.306606{col 53}{space 3} 1.510598
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. mean TTNRInfonoreturn, over(country),  if online==1
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}       981

       {txt}France: country = {res}France
        {txt}Spain: country = {res}Spain
    {txt}_subpop_3: country = {res}Turkey online
           {txt}UK: country = {res}UK
           {txt}US: country = {res}US

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 1}            Over{col 18}{c |}       Mean{col 30}   Std. Err.{col 42}     [95% Con{col 55}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{res}TTNRInfonoreturn {txt}{c |}
{space 10}France {c |}{col 18}{res}{space 2} 1.478261{col 30}{space 2} .0474814{col 41}{space 5} 1.385084{col 55}{space 3} 1.571438
{txt}{space 11}Spain {c |}{col 18}{res}{space 2} 1.419689{col 30}{space 2} .0473286{col 41}{space 5} 1.326812{col 55}{space 3} 1.512566
{txt}{space 7}_subpop_3 {c |}{col 18}{res}{space 2}  1.44335{col 30}{space 2} .0430789{col 41}{space 5} 1.358812{col 55}{space 3} 1.527887
{txt}{space 14}UK {c |}{col 18}{res}{space 2}     1.22{col 30}{space 2} .0497026{col 41}{space 5} 1.122464{col 55}{space 3} 1.317536
{txt}{space 14}US {c |}{col 18}{res}{space 2}  1.40796{col 30}{space 2} .0447623{col 41}{space 5} 1.320119{col 55}{space 3} 1.495801
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. mean TTRInfonoreturn, over(country),  if online==1
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}       944

       {txt}France: country = {res}France
        {txt}Spain: country = {res}Spain
    {txt}_subpop_3: country = {res}Turkey online
           {txt}UK: country = {res}UK
           {txt}US: country = {res}US

{txt}{hline 16}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 1}           Over{col 17}{c |}       Mean{col 29}   Std. Err.{col 41}     [95% Con{col 54}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{res}TTRInfonoreturn {txt}{c |}
{space 9}France {c |}{col 17}{res}{space 2} 1.471795{col 29}{space 2}  .046726{col 40}{space 5} 1.380096{col 54}{space 3} 1.563494
{txt}{space 10}Spain {c |}{col 17}{res}{space 2} 1.370166{col 29}{space 2} .0502293{col 40}{space 5} 1.271592{col 54}{space 3}  1.46874
{txt}{space 6}_subpop_3 {c |}{col 17}{res}{space 2} 1.413613{col 29}{space 2}  .047629{col 40}{space 5} 1.320141{col 54}{space 3} 1.507084
{txt}{space 13}UK {c |}{col 17}{res}{space 2}     1.29{col 29}{space 2} .0473361{col 40}{space 5} 1.197104{col 54}{space 3} 1.382896
{txt}{space 13}US {c |}{col 17}{res}{space 2} 1.412429{col 29}{space 2} .0490317{col 40}{space 5} 1.316206{col 54}{space 3} 1.508653
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. *below generates the Chi2 results reported in Appendix Table 6
. gen Infonoreturn=.
{txt}(7,746 missing values generated)

{com}. recode Infonoreturn .=2 if GTInfonoreturn==2
{txt}(Infonoreturn: 567 changes made)

{com}. recode Infonoreturn .=1 if GTInfonoreturn==1
{txt}(Infonoreturn: 452 changes made)

{com}. recode Infonoreturn .=0 if GTInfonoreturn==0
{txt}(Infonoreturn: 411 changes made)

{com}. recode Infonoreturn .=2 if TTNRInfonoreturn==2
{txt}(Infonoreturn: 766 changes made)

{com}. recode Infonoreturn .=1 if TTNRInfonoreturn==1
{txt}(Infonoreturn: 544 changes made)

{com}. recode Infonoreturn .=0 if TTNRInfonoreturn==0
{txt}(Infonoreturn: 169 changes made)

{com}. recode Infonoreturn .=2 if TTRInfonoreturn==2
{txt}(Infonoreturn: 743 changes made)

{com}. recode Infonoreturn .=1 if TTRInfonoreturn==1
{txt}(Infonoreturn: 528 changes made)

{com}. recode Infonoreturn .=0 if TTRInfonoreturn==0
{txt}(Infonoreturn: 162 changes made)

{com}. 
. by country: tabulate Infonoreturn GT_1vsTTNR_0, chi2, if online==1

{txt}{hline}
-> country = Albania
no observations

{hline}
-> country = Ecuador
no observations

{hline}
-> country = France

Infonoretu {c |}     GT_1vsTTNR_0
        rn {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        15         51 {txt}{c |}{res}        66 
{txt}         1 {c |}{res}        66         52 {txt}{c |}{res}       118 
{txt}         2 {c |}{res}       103         80 {txt}{c |}{res}       183 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       184        183 {txt}{c |}{res}       367 

{txt}          Pearson chi2({res}2{txt}) = {res} 24.1855  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Peru
no observations

{hline}
-> country = Spain

Infonoretu {c |}     GT_1vsTTNR_0
        rn {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        18         53 {txt}{c |}{res}        71 
{txt}         1 {c |}{res}        76         67 {txt}{c |}{res}       143 
{txt}         2 {c |}{res}        99         74 {txt}{c |}{res}       173 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       193        194 {txt}{c |}{res}       387 

{txt}          Pearson chi2({res}2{txt}) = {res} 21.4302  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Turkey FtoF
no observations

{hline}
-> country = Turkey online

Infonoretu {c |}     GT_1vsTTNR_0
        rn {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        13         40 {txt}{c |}{res}        53 
{txt}         1 {c |}{res}        87         66 {txt}{c |}{res}       153 
{txt}         2 {c |}{res}       103         79 {txt}{c |}{res}       182 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       203        185 {txt}{c |}{res}       388 

{txt}          Pearson chi2({res}2{txt}) = {res} 19.0078  {txt} Pr = {res}0.000

{txt}{hline}
-> country = UK

Infonoretu {c |}     GT_1vsTTNR_0
        rn {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        32         47 {txt}{c |}{res}        79 
{txt}         1 {c |}{res}        92         73 {txt}{c |}{res}       165 
{txt}         2 {c |}{res}        76         70 {txt}{c |}{res}       146 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       200        190 {txt}{c |}{res}       390 

{txt}          Pearson chi2({res}2{txt}) = {res}  5.0295  {txt} Pr = {res}0.081

{txt}{hline}
-> country = US

Infonoretu {c |}     GT_1vsTTNR_0
        rn {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        16         24 {txt}{c |}{res}        40 
{txt}         1 {c |}{res}        87         62 {txt}{c |}{res}       149 
{txt}         2 {c |}{res}        98        100 {txt}{c |}{res}       198 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       201        186 {txt}{c |}{res}       387 

{txt}          Pearson chi2({res}2{txt}) = {res}  5.2413  {txt} Pr = {res}0.073

{txt}
{com}. by country: tabulate Infonoreturn GT_1vsTTR_0, chi2, if online==1

{txt}{hline}
-> country = Albania
no observations

{hline}
-> country = Ecuador
no observations

{hline}
-> country = France

Infonoretu {c |}      GT_1vsTTR_0
        rn {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        17         51 {txt}{c |}{res}        68 
{txt}         1 {c |}{res}        69         52 {txt}{c |}{res}       121 
{txt}         2 {c |}{res}       109         80 {txt}{c |}{res}       189 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       195        183 {txt}{c |}{res}       378 

{txt}          Pearson chi2({res}2{txt}) = {res} 23.4809  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Peru
no observations

{hline}
-> country = Spain

Infonoretu {c |}      GT_1vsTTR_0
        rn {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        20         53 {txt}{c |}{res}        73 
{txt}         1 {c |}{res}        74         67 {txt}{c |}{res}       141 
{txt}         2 {c |}{res}        87         74 {txt}{c |}{res}       161 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       181        194 {txt}{c |}{res}       375 

{txt}          Pearson chi2({res}2{txt}) = {res} 15.8834  {txt} Pr = {res}0.000

{txt}{hline}
-> country = Turkey FtoF
no observations

{hline}
-> country = Turkey online

Infonoretu {c |}      GT_1vsTTR_0
        rn {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        18         40 {txt}{c |}{res}        58 
{txt}         1 {c |}{res}        76         66 {txt}{c |}{res}       142 
{txt}         2 {c |}{res}        97         79 {txt}{c |}{res}       176 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       191        185 {txt}{c |}{res}       376 

{txt}          Pearson chi2({res}2{txt}) = {res} 10.7970  {txt} Pr = {res}0.005

{txt}{hline}
-> country = UK

Infonoretu {c |}      GT_1vsTTR_0
        rn {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        24         47 {txt}{c |}{res}        71 
{txt}         1 {c |}{res}        94         73 {txt}{c |}{res}       167 
{txt}         2 {c |}{res}        82         70 {txt}{c |}{res}       152 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       200        190 {txt}{c |}{res}       390 

{txt}          Pearson chi2({res}2{txt}) = {res} 10.7895  {txt} Pr = {res}0.005

{txt}{hline}
-> country = US

Infonoretu {c |}      GT_1vsTTR_0
        rn {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}        16         24 {txt}{c |}{res}        40 
{txt}         1 {c |}{res}        72         62 {txt}{c |}{res}       134 
{txt}         2 {c |}{res}        89        100 {txt}{c |}{res}       189 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       177        186 {txt}{c |}{res}       363 

{txt}          Pearson chi2({res}2{txt}) = {res}  2.7650  {txt} Pr = {res}0.251

{txt}
{com}. 
. 
. *********************************
. *******GENERATE OUTPUT FOR*******
. ************ FIGURE 3 ***********
. *********************************
. 
. * below generates the "motivated to return" output for figure 3
. 
. mean gt_return, over(country), if online==1
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}       938

       {txt}France: country = {res}France
        {txt}Spain: country = {res}Spain
    {txt}_subpop_3: country = {res}Turkey online
           {txt}UK: country = {res}UK
           {txt}US: country = {res}US

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 1}        Over{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{res}gt_return    {txt}{c |}
{space 6}France {c |}{col 14}{res}{space 2} .1857923{col 26}{space 2} .0318013{col 37}{space 5} .1233824{col 51}{space 3} .2482023
{txt}{space 7}Spain {c |}{col 14}{res}{space 2} .2268041{col 26}{space 2} .0326936{col 37}{space 5}  .162643{col 51}{space 3} .2909652
{txt}{space 3}_subpop_3 {c |}{col 14}{res}{space 2} .3243243{col 26}{space 2} .0407554{col 37}{space 5} .2443419{col 51}{space 3} .4043068
{txt}{space 10}UK {c |}{col 14}{res}{space 2} .3210526{col 26}{space 2} .0378391{col 37}{space 5} .2467935{col 51}{space 3} .3953118
{txt}{space 10}US {c |}{col 14}{res}{space 2} .2419355{col 26}{space 2} .0341428{col 37}{space 5} .1749302{col 51}{space 3} .3089408
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. mean ttnr_return, over(country), if online==1
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}       981

       {txt}France: country = {res}France
        {txt}Spain: country = {res}Spain
    {txt}_subpop_3: country = {res}Turkey online
           {txt}UK: country = {res}UK
           {txt}US: country = {res}US

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 1}        Over{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{res}ttnr_return  {txt}{c |}
{space 6}France {c |}{col 14}{res}{space 2} .2880435{col 26}{space 2}  .039197{col 37}{space 5} .2111239{col 51}{space 3} .3649631
{txt}{space 7}Spain {c |}{col 14}{res}{space 2} .3160622{col 26}{space 2} .0373595{col 37}{space 5} .2427484{col 51}{space 3}  .389376
{txt}{space 3}_subpop_3 {c |}{col 14}{res}{space 2} .3546798{col 26}{space 2} .0371072{col 37}{space 5}  .281861{col 51}{space 3} .4274986
{txt}{space 10}UK {c |}{col 14}{res}{space 2}      .45{col 26}{space 2} .0429736{col 37}{space 5} .3656691{col 51}{space 3} .5343309
{txt}{space 10}US {c |}{col 14}{res}{space 2} .4228856{col 26}{space 2} .0402278{col 37}{space 5}  .343943{col 51}{space 3} .5018281
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. mean ttr_return, over(country), if online==1
{res}
{txt}Mean estimation{col 35}Number of obs{col 51}= {res}       941

       {txt}France: country = {res}France
        {txt}Spain: country = {res}Spain
    {txt}_subpop_3: country = {res}Turkey online
           {txt}UK: country = {res}UK
           {txt}US: country = {res}US

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 14}{hline 12}
{col 1}        Over{col 14}{c |}       Mean{col 26}   Std. Err.{col 38}     [95% Con{col 51}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 14}{hline 12}
{res}ttr_return   {txt}{c |}
{space 6}France {c |}{col 14}{res}{space 2} .2205128{col 26}{space 2} .0323206{col 37}{space 5}  .157084{col 51}{space 3} .2839416
{txt}{space 7}Spain {c |}{col 14}{res}{space 2}  .320442{col 26}{space 2} .0397253{col 37}{space 5} .2424815{col 51}{space 3} .3984024
{txt}{space 3}_subpop_3 {c |}{col 14}{res}{space 2} .4391534{col 26}{space 2} .0451866{col 37}{space 5} .3504751{col 51}{space 3} .5278317
{txt}{space 10}UK {c |}{col 14}{res}{space 2}     .455{col 26}{space 2} .0430014{col 37}{space 5} .3706101{col 51}{space 3} .5393899
{txt}{space 10}US {c |}{col 14}{res}{space 2} .3636364{col 26}{space 2}  .039775{col 37}{space 5} .2855782{col 51}{space 3} .4416945
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 14}{hline 12}

{com}. 
. * below runs the Chi2 tests discussed in text on above output
. * these are also reported on in Appendix Table 6
. 
. by country: tabulate return GT_1vsTTNR_0, chi2, if online==1

{txt}{hline}
-> country = Albania
no observations

{hline}
-> country = Ecuador
no observations

{hline}
-> country = France

           {c |}     GT_1vsTTNR_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       138        152 {txt}{c |}{res}       290 
{txt}         1 {c |}{res}        39         28 {txt}{c |}{res}        67 
{txt}         2 {c |}{res}         7          3 {txt}{c |}{res}        10 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       184        183 {txt}{c |}{res}       367 

{txt}          Pearson chi2({res}2{txt}) = {res}  4.0791  {txt} Pr = {res}0.130

{txt}{hline}
-> country = Peru
no observations

{hline}
-> country = Spain

           {c |}     GT_1vsTTNR_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       137        153 {txt}{c |}{res}       290 
{txt}         1 {c |}{res}        51         38 {txt}{c |}{res}        89 
{txt}         2 {c |}{res}         5          3 {txt}{c |}{res}         8 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       193        194 {txt}{c |}{res}       387 

{txt}          Pearson chi2({res}2{txt}) = {res}  3.2791  {txt} Pr = {res}0.194

{txt}{hline}
-> country = Turkey FtoF
no observations

{hline}
-> country = Turkey online

           {c |}     GT_1vsTTNR_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       136        133 {txt}{c |}{res}       269 
{txt}         1 {c |}{res}        62         44 {txt}{c |}{res}       106 
{txt}         2 {c |}{res}         5          8 {txt}{c |}{res}        13 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       203        185 {txt}{c |}{res}       388 

{txt}          Pearson chi2({res}2{txt}) = {res}  2.9537  {txt} Pr = {res}0.228

{txt}{hline}
-> country = UK

           {c |}     GT_1vsTTNR_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       122        134 {txt}{c |}{res}       256 
{txt}         1 {c |}{res}        66         51 {txt}{c |}{res}       117 
{txt}         2 {c |}{res}        12          5 {txt}{c |}{res}        17 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       200        190 {txt}{c |}{res}       390 

{txt}          Pearson chi2({res}2{txt}) = {res}  5.1149  {txt} Pr = {res}0.078

{txt}{hline}
-> country = US

           {c |}     GT_1vsTTNR_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       124        144 {txt}{c |}{res}       268 
{txt}         1 {c |}{res}        69         39 {txt}{c |}{res}       108 
{txt}         2 {c |}{res}         8          3 {txt}{c |}{res}        11 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       201        186 {txt}{c |}{res}       387 

{txt}          Pearson chi2({res}2{txt}) = {res} 11.5345  {txt} Pr = {res}0.003

{txt}
{com}. by country: tabulate return GT_1vsTTR_0, chi2, if online==1

{txt}{hline}
-> country = Albania
no observations

{hline}
-> country = Ecuador
no observations

{hline}
-> country = France

           {c |}      GT_1vsTTR_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       155        152 {txt}{c |}{res}       307 
{txt}         1 {c |}{res}        37         28 {txt}{c |}{res}        65 
{txt}         2 {c |}{res}         3          3 {txt}{c |}{res}         6 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       195        183 {txt}{c |}{res}       378 

{txt}          Pearson chi2({res}2{txt}) = {res}  0.8954  {txt} Pr = {res}0.639

{txt}{hline}
-> country = Peru
no observations

{hline}
-> country = Spain

           {c |}      GT_1vsTTR_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       129        153 {txt}{c |}{res}       282 
{txt}         1 {c |}{res}        46         38 {txt}{c |}{res}        84 
{txt}         2 {c |}{res}         6          3 {txt}{c |}{res}         9 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       181        194 {txt}{c |}{res}       375 

{txt}          Pearson chi2({res}2{txt}) = {res}  3.3578  {txt} Pr = {res}0.187

{txt}{hline}
-> country = Turkey FtoF
no observations

{hline}
-> country = Turkey online

           {c |}      GT_1vsTTR_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       119        133 {txt}{c |}{res}       252 
{txt}         1 {c |}{res}        57         44 {txt}{c |}{res}       101 
{txt}         2 {c |}{res}        13          8 {txt}{c |}{res}        21 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       189        185 {txt}{c |}{res}       374 

{txt}          Pearson chi2({res}2{txt}) = {res}  3.5992  {txt} Pr = {res}0.165

{txt}{hline}
-> country = UK

           {c |}      GT_1vsTTR_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       121        134 {txt}{c |}{res}       255 
{txt}         1 {c |}{res}        67         51 {txt}{c |}{res}       118 
{txt}         2 {c |}{res}        12          5 {txt}{c |}{res}        17 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       200        190 {txt}{c |}{res}       390 

{txt}          Pearson chi2({res}2{txt}) = {res}  5.4618  {txt} Pr = {res}0.065

{txt}{hline}
-> country = US

           {c |}      GT_1vsTTR_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       116        144 {txt}{c |}{res}       260 
{txt}         1 {c |}{res}        56         39 {txt}{c |}{res}        95 
{txt}         2 {c |}{res}         4          3 {txt}{c |}{res}         7 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       176        186 {txt}{c |}{res}       362 

{txt}          Pearson chi2({res}2{txt}) = {res}  5.9286  {txt} Pr = {res}0.052

{txt}
{com}. 
. * below produces output discussed in text within this section:
. 
. by country: sum et_return if online==1

{txt}{hline}
-> country = Albania

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}et_return {c |}{res}          0

{txt}{hline}
-> country = Ecuador

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}et_return {c |}{res}          0

{txt}{hline}
-> country = France

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}et_return {c |}{res}        188    .2553191    .5157658          0          2

{txt}{hline}
-> country = Peru

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}et_return {c |}{res}          0

{txt}{hline}
-> country = Spain

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}et_return {c |}{res}        187    .3208556    .5523589          0          2

{txt}{hline}
-> country = Turkey FtoF

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}et_return {c |}{res}          0

{txt}{hline}
-> country = Turkey online

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}et_return {c |}{res}        176    .4318182    .6008652          0          2

{txt}{hline}
-> country = UK

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}et_return {c |}{res}        204    .3872549    .6448235          0          2

{txt}{hline}
-> country = US

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}et_return {c |}{res}        196    .3928571    .6273673          0          2

{txt}
{com}. by country: sum dt_return if online==1

{txt}{hline}
-> country = Albania

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}dt_return {c |}{res}          0

{txt}{hline}
-> country = Ecuador

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}dt_return {c |}{res}          0

{txt}{hline}
-> country = France

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}dt_return {c |}{res}          0

{txt}{hline}
-> country = Peru

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}dt_return {c |}{res}          0

{txt}{hline}
-> country = Spain

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}dt_return {c |}{res}        192     .390625    .5496459          0          2

{txt}{hline}
-> country = Turkey FtoF

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}dt_return {c |}{res}          0

{txt}{hline}
-> country = Turkey online

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}dt_return {c |}{res}          0

{txt}{hline}
-> country = UK

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}dt_return {c |}{res}          0

{txt}{hline}
-> country = US

    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 3}dt_return {c |}{res}          0

{txt}
{com}. by country: tabulate return GT_1vsDT_0, chi2, if online==1

{txt}{hline}
-> country = Albania
no observations

{hline}
-> country = Ecuador
no observations

{hline}
-> country = France

           {c |} GT_1vsDT_0
    return {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}       152 {txt}{c |}{res}       152 
{txt}         1 {c |}{res}        28 {txt}{c |}{res}        28 
{txt}         2 {c |}{res}         3 {txt}{c |}{res}         3 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       183 {txt}{c |}{res}       183 


{txt}{hline}
-> country = Peru
no observations

{hline}
-> country = Spain

           {c |}      GT_1vsDT_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       123        153 {txt}{c |}{res}       276 
{txt}         1 {c |}{res}        63         38 {txt}{c |}{res}       101 
{txt}         2 {c |}{res}         6          3 {txt}{c |}{res}         9 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       192        194 {txt}{c |}{res}       386 

{txt}          Pearson chi2({res}2{txt}) = {res} 10.4389  {txt} Pr = {res}0.005

{txt}{hline}
-> country = Turkey FtoF
no observations

{hline}
-> country = Turkey online

           {c |} GT_1vsDT_0
    return {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}       133 {txt}{c |}{res}       133 
{txt}         1 {c |}{res}        44 {txt}{c |}{res}        44 
{txt}         2 {c |}{res}         8 {txt}{c |}{res}         8 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       185 {txt}{c |}{res}       185 


{txt}{hline}
-> country = UK

           {c |} GT_1vsDT_0
    return {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}       134 {txt}{c |}{res}       134 
{txt}         1 {c |}{res}        51 {txt}{c |}{res}        51 
{txt}         2 {c |}{res}         5 {txt}{c |}{res}         5 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       190 {txt}{c |}{res}       190 


{txt}{hline}
-> country = US

           {c |} GT_1vsDT_0
    return {c |}         1 {c |}     Total
{hline 11}{c +}{hline 11}{c +}{hline 10}
         0 {c |}{res}       144 {txt}{c |}{res}       144 
{txt}         1 {c |}{res}        39 {txt}{c |}{res}        39 
{txt}         2 {c |}{res}         3 {txt}{c |}{res}         3 
{txt}{hline 11}{c +}{hline 11}{c +}{hline 10}
     Total {c |}{res}       186 {txt}{c |}{res}       186 


{txt}
{com}. by country: tabulate return GT_1vsET_0, chi2, if online==1

{txt}{hline}
-> country = Albania
no observations

{hline}
-> country = Ecuador
no observations

{hline}
-> country = France

           {c |}      GT_1vsET_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       147        152 {txt}{c |}{res}       299 
{txt}         1 {c |}{res}        34         28 {txt}{c |}{res}        62 
{txt}         2 {c |}{res}         7          3 {txt}{c |}{res}        10 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       188        183 {txt}{c |}{res}       371 

{txt}          Pearson chi2({res}2{txt}) = {res}  2.1973  {txt} Pr = {res}0.333

{txt}{hline}
-> country = Peru
no observations

{hline}
-> country = Spain

           {c |}      GT_1vsET_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       135        153 {txt}{c |}{res}       288 
{txt}         1 {c |}{res}        44         38 {txt}{c |}{res}        82 
{txt}         2 {c |}{res}         8          3 {txt}{c |}{res}        11 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       187        194 {txt}{c |}{res}       381 

{txt}          Pearson chi2({res}2{txt}) = {res}  3.7094  {txt} Pr = {res}0.157

{txt}{hline}
-> country = Turkey FtoF
no observations

{hline}
-> country = Turkey online

           {c |}      GT_1vsET_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       110        133 {txt}{c |}{res}       243 
{txt}         1 {c |}{res}        56         44 {txt}{c |}{res}       100 
{txt}         2 {c |}{res}        10          8 {txt}{c |}{res}        18 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       176        185 {txt}{c |}{res}       361 

{txt}          Pearson chi2({res}2{txt}) = {res}  3.6170  {txt} Pr = {res}0.164

{txt}{hline}
-> country = UK

           {c |}      GT_1vsET_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       143        134 {txt}{c |}{res}       277 
{txt}         1 {c |}{res}        43         51 {txt}{c |}{res}        94 
{txt}         2 {c |}{res}        18          5 {txt}{c |}{res}        23 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       204        190 {txt}{c |}{res}       394 

{txt}          Pearson chi2({res}2{txt}) = {res}  7.8335  {txt} Pr = {res}0.020

{txt}{hline}
-> country = US

           {c |}      GT_1vsET_0
    return {c |}         0          1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
         0 {c |}{res}       134        144 {txt}{c |}{res}       278 
{txt}         1 {c |}{res}        47         39 {txt}{c |}{res}        86 
{txt}         2 {c |}{res}        15          3 {txt}{c |}{res}        18 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       196        186 {txt}{c |}{res}       382 

{txt}          Pearson chi2({res}2{txt}) = {res}  8.8482  {txt} Pr = {res}0.012

{txt}
{com}. 
. 
.           
. 
. 
. 
{txt}end of do-file

{com}. 